Reliability evaluation system and evaluation method for medium-voltage power distribution network frame
The reliability assessment system for medium-voltage distribution network structures solves the problems of insufficient data collection and simplified assessment models in existing technologies. It enables accurate assessment and optimization of complex network structures, improves the resilience and power supply stability of the power system, reduces costs, and ensures power supply quality and safety.
Patent Information
- Application Number
- CN202510979942.7
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-07-16
- Publication Date
- 2025-11-11
AI Technical Summary
Existing reliability assessments of medium-voltage distribution networks rely on empirical judgments or simplified models, which are difficult to adapt to the actual needs of complex network structures. They also suffer from insufficient data acquisition capabilities, oversimplified assessment models, and weak ability to cope with extreme scenarios.
A reliability assessment system for medium-voltage distribution network is provided, including a data acquisition and monitoring module, a fault diagnosis module, an assessment model module, a simulation and optimization module, a decision support module, and a reporting and assessment module. Through real-time monitoring, machine learning, simulation, and optimization, it identifies potential weak links and optimizes power grid configuration and emergency plans.
This has enabled a leap from static assessment to dynamic prediction, enhancing the resilience and intelligence of the power grid, reducing the total life-cycle cost, ensuring power supply quality and public safety, and improving the safety and power supply quality of the power system.
Smart Images
Figure CN120934175A_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of distribution network reliability assessment technology, specifically to a reliability assessment system and method for a medium-voltage distribution network structure. Background Technology
[0002] Medium-voltage distribution networks (typically referring to 10-35kV voltage levels) are a crucial link connecting high-voltage transmission networks with low-voltage user sides, and their reliability directly affects social production, residents' lives, and public safety. The development of reliability assessment systems and methods for medium-voltage distribution network structures stems from the core requirements of power systems for power supply stability, the real challenges of socio-economic development, and the driving force of technological iteration.
[0003] Medium-voltage distribution networks constitute the most extensive, structurally complex, and directly connected part of the power system, handling approximately 80% of power distribution. Their reliability indicators (such as SAIDI, SAIFI, and CAIDI) directly reflect power supply quality and are core standards for measuring the service level of the power system. With socio-economic development, users' demands for power supply reliability continue to escalate. Industrial users: Precision manufacturing, data centers, etc. are sensitive to power outage time and require annual power outage time to be less than 5 minutes; Residential users: The widespread adoption of smart home appliances and electric vehicles has increased the demand for a "zero power outage" experience; Public services: Hospitals, transportation hubs and other critical locations need to be guaranteed "uninterrupted power supply", as power outages may cause safety accidents.
[0004] Early reliability assessments of medium-voltage distribution networks relied on empirical judgments or simplified models, which were difficult to adapt to the actual needs of complex network structures. The main challenges included: 1. Insufficient data collection capabilities: Traditional assessments rely on manual inspection records or offline ledgers, resulting in problems such as "data lag, incomplete coverage, and large errors." 2. The assessment model is oversimplified. Traditional methods often use "Failure Mode and Effects Analysis (FMEA)" or "analytical methods", but these have obvious limitations. 3. Weak ability to cope with extreme scenarios. Climate change has led to frequent extreme weather events (typhoons, rainstorms, freezing), significantly increasing the probability of faults in medium-voltage power distribution networks.
[0005] Therefore, those skilled in the art provide a reliability assessment system and method for medium-voltage distribution network structures to address the problems mentioned in the background art. Summary of the Invention
[0006] To address the shortcomings of existing technologies, this invention provides a reliability assessment system and method for medium-voltage distribution network structures, solving the problem that early reliability assessments of medium-voltage distribution networks relied on empirical judgments or simplified models, making it difficult to adapt to the actual needs of complex network structures.
[0007] To achieve the above objectives, the present invention provides the following technical solution: A reliability assessment system for a medium-voltage distribution network includes a data acquisition and monitoring module, a fault diagnosis module, an assessment model module, a simulation and optimization module, a decision support module, and a reporting and assessment module. Data acquisition and monitoring module: By installing various sensors and monitoring equipment, it monitors various operating parameters in the power grid in real time, including current, voltage, and power, to collect key data such as current, voltage, and load in the distribution network; Fault diagnosis module: It uses fault diagnosis technology and machine learning algorithms to predict possible equipment failures, take measures in advance to reduce power outages and power losses, and identify and locate fault points in the distribution network to help restore power supply quickly; The evaluation model module calculates the reliability index of the distribution network through statistical, probability theory and power system models, and analyzes the data to evaluate the reliability of various parts of the distribution network (such as transformers, switches, lines, etc.) and identify potential weak links in the system. The simulation and optimization module simulates the distribution network structure and analyzes its operation under different conditions to help optimize the power grid configuration and emergency plans. The decision support module provides support for grid dispatch, operation and maintenance, and upgrades based on the assessment results, and provides power companies with decision support for reliability improvement, including equipment maintenance, replacement, and optimized dispatch. The reporting and evaluation module generates reports based on evaluation results and proposes improvement plans to help power companies improve the reliability of the distribution network and reduce the risk of power outages.
[0008] Furthermore, the data acquisition and monitoring module mainly collects historical fault data, including equipment failure rate, downtime, fault type, and repair time. It can also collect load data, including load data of each node in the distribution network, especially load fluctuations during peak hours and under special circumstances.
[0009] Furthermore, the data acquisition and monitoring module can also monitor data status, including the operating status and health status of equipment such as transformers, switches, and lines.
[0010] Furthermore, the specific implementation process of the fault diagnosis module is as follows: 1) By monitoring parameters such as current and voltage in real time, abnormal changes in waveforms can be analyzed to detect whether there is a equipment malfunction; 2) Utilize intelligent sensors (such as temperature sensors, vibration sensors, gas sensors, etc. in substations) to monitor the operating status of equipment in real time and detect abnormalities in electrical equipment (such as overload, insulation aging, poor contact, etc.) at an early stage. 3) By applying pattern recognition, machine learning, and data mining techniques, identify and classify fault patterns; for example, analyze historical fault data based on algorithms such as support vector machine (SVM) and decision tree (DT) to help pinpoint the likelihood and severity of fault occurrence.
[0011] Furthermore, the specific implementation process of the evaluation model module is as follows: 1) Simulate the operating state of the power grid by using the topology of the power grid (such as nodes, branches, transformers, etc.) and analyze the load of each device and its impact on the overall reliability of the system; 2) By discretizing the power grid operating state, a mathematical model of the power grid state is established to simulate the impact of equipment failures, load changes, etc. on the reliability of the power grid; 3) Based on random sampling technology, the reliability of the distribution network can be evaluated by simulating a large number of possible operating scenarios, which can effectively handle the uncertainties and randomness in the system.
[0012] Furthermore, the simulation and optimization module can also propose equipment replacement or maintenance suggestions based on equipment failure rate and repair time, optimize the power grid topology based on reliability analysis, reduce the impact of fault points on the overall reliability of the power grid, and suggest setting backup lines or equipment in critical locations to improve system redundancy and ensure that the power grid can recover quickly in the event of a fault.
[0013] Furthermore, the reporting and evaluation module includes the distribution network reliability status, optimization suggestions, and future reliability forecasts; The power distribution network reliability status is used to demonstrate the health status of the power grid, assess the reliability of equipment, and analyze potential risks. The optimization recommendations are used to propose specific suggestions for system optimization, equipment maintenance, and replacement based on the evaluation results; The future reliability forecast can predict future trends in power grid reliability based on existing data, helping power companies to develop countermeasures in advance.
[0014] Furthermore, a reliability assessment method for a medium-voltage distribution network includes the following steps: Step S1. Data Collection and Preprocessing Historical fault data of medium-voltage distribution network equipment is collected, including equipment failure rate, outage time, fault type, and repair time. Load data of power grid equipment is also collected, including load data of each node of the distribution network, especially load fluctuations during peak hours and under special circumstances. The status of power grid equipment is then monitored, including the operating status and health status of equipment such as transformers, switches, and lines. Step S2. Reliability Model Establishment Select an appropriate assessment method based on the assessment objectives, set the failure rate, maintenance time, and load level parameters for each component based on the collected data, determine the network topology of the distribution network, and identify key equipment and load nodes. Step S3. Evaluation and Analysis Using the selected evaluation method, calculate various reliability indicators of the distribution network. Through Monte Carlo simulation, simulate the system operation under different fault scenarios and load conditions, evaluate the reliability of the system under different conditions, analyze equipment or areas with high fault risk, predict possible power outage events, and prepare emergency plans in advance. Step S4. Result Analysis and Optimization By assessing the results, weaknesses and potential risk areas in the distribution network are identified, key factors affecting reliability are found, and the design (such as adding backup equipment and optimizing load distribution) and operation and maintenance strategies (such as strengthening inspection and maintenance in high-failure-rate areas) of the distribution network are optimized based on the assessment results. Targeted improvement measures are then implemented to reduce the occurrence of faults and improve the stability and fault resistance of the distribution network. Step S5. Regular evaluation and updates Regular reliability assessments should be conducted on power grid equipment to ensure that the distribution network continuously meets the requirements for safe, stable and reliable power supply. Step S6. Feedback on Evaluation Results Based on the assessment results, the design, operation, and maintenance strategies of the distribution network should be adjusted in a timely manner to ensure that the system is in optimal condition in the long term.
[0015] This invention provides a reliability assessment system and method for medium-voltage distribution network structures. It offers the following advantages: 1. This invention provides a reliability assessment system and method for medium-voltage distribution network, which plays a crucial role in the planning, design, operation and maintenance of power systems. Through accurate assessment methods, the reliability and stability of the distribution network can be ensured, thereby improving the overall safety and power supply quality of the power system.
[0016] 2. This invention provides a reliability assessment system and method for medium-voltage distribution network. The process of comprehensively assessing the system through multiple methods aims to identify potential fault points, optimize the operation of the distribution network, and improve the stability and reliability of power supply. Through the system's assessment methods and processes, the power system can better cope with possible faults and emergencies, thereby ensuring that customers' electricity needs are reliably guaranteed.
[0017] This invention provides a reliability assessment system and method for medium-voltage distribution network structures. At the technical level, it achieves a leap from static assessment to dynamic prediction, improving the resilience and intelligence level of the network structure. At the economic level, it significantly reduces the total life cycle cost through cost-benefit analysis and operation and maintenance optimization. At the social level, it ensures power supply quality, enhances public safety, and provides support for energy transition. Attached Figure Description
[0018] Figure 1 This is a structural diagram of the reliability assessment system for the medium-voltage distribution network structure of the present invention; Figure 2 This is a flowchart of the reliability assessment method for medium-voltage distribution network structure according to the present invention. Detailed Implementation
[0019] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention. Example
[0020] like Figure 1 As shown, this embodiment of the invention provides a reliability assessment system for a medium-voltage distribution network, including a data acquisition and monitoring module, a fault diagnosis module, an assessment model module, a simulation and optimization module, a decision support module, and a reporting and assessment module; Data acquisition and monitoring module: By installing various sensors and monitoring equipment, it monitors various operating parameters in the power grid in real time, including current, voltage, and power, to collect key data such as current, voltage, and load in the distribution network; The data acquisition and monitoring module mainly collects historical fault data, including equipment failure rate, downtime, fault type, and repair time. It can also collect load data, including load data of each node in the distribution network, especially load fluctuations during peak hours and under special circumstances. The data acquisition and monitoring module can also monitor data status, including the operating status and health status of equipment such as transformers, switches, and lines.
[0021] Fault diagnosis module: It uses fault diagnosis technology and machine learning algorithms to predict possible equipment failures, take measures in advance to reduce power outages and power losses, and identify and locate fault points in the distribution network to help restore power supply quickly; The specific implementation process of the fault diagnosis module is as follows: 1) By monitoring parameters such as current and voltage in real time, abnormal changes in waveforms can be analyzed to detect whether there is a equipment malfunction; 2) Utilize intelligent sensors (such as temperature sensors, vibration sensors, gas sensors, etc. in substations) to monitor the operating status of equipment in real time and detect abnormalities in electrical equipment (such as overload, insulation aging, poor contact, etc.) at an early stage. 3) By applying pattern recognition, machine learning, and data mining techniques, identify and classify fault patterns; for example, analyze historical fault data based on algorithms such as support vector machine (SVM) and decision tree (DT) to help pinpoint the likelihood and severity of fault occurrence.
[0022] The evaluation model module calculates the reliability index of the distribution network through statistical, probability theory and power system models, and analyzes the data to evaluate the reliability of various parts of the distribution network (such as transformers, switches, lines, etc.) and identify potential weak links in the system. The specific implementation process of the evaluation model module is as follows: 1) Simulate the operating state of the power grid by using the topology of the power grid (such as nodes, branches, transformers, etc.) and analyze the load of each device and its impact on the overall reliability of the system; 2) By discretizing the power grid operating state, a mathematical model of the power grid state is established to simulate the impact of equipment failures, load changes, etc. on the reliability of the power grid; 3) Based on random sampling technology, the reliability of the distribution network can be evaluated by simulating a large number of possible operating scenarios, which can effectively handle the uncertainties and randomness in the system.
[0023] The simulation and optimization module simulates the distribution network structure and analyzes its operation under different conditions to help optimize the power grid configuration and emergency plans. The simulation and optimization module can also propose equipment replacement or maintenance suggestions based on equipment failure rate and repair time. Based on reliability analysis, it can optimize the topology of the power grid, reduce the impact of failure points on the overall reliability of the power grid, and suggest setting up backup lines or equipment in critical locations to improve system redundancy and ensure that the power grid can recover quickly in the event of a failure.
[0024] The decision support module provides support for grid dispatch, operation and maintenance, and upgrades based on the assessment results, and provides power companies with decision support for reliability improvement, including equipment maintenance, replacement, and optimized dispatch. The reporting and evaluation module generates reports based on evaluation results and proposes improvement plans to help power companies improve the reliability of the distribution network and reduce the risk of power outages. The reporting and assessment module includes the distribution network reliability status, optimization recommendations, and future reliability forecasts; The power distribution network reliability status is used to demonstrate the health status of the power grid, assess the reliability of equipment, and analyze potential risks. The optimization recommendations are used to propose specific suggestions for system optimization, equipment maintenance, and replacement based on the evaluation results; The future reliability forecast can predict future trends in power grid reliability based on existing data, helping power companies to develop countermeasures in advance.
[0025] like Figure 2 As shown, the reliability assessment method for this medium-voltage distribution network includes the following steps: Step S1. Data Collection and Preprocessing Historical fault data of medium-voltage distribution network equipment is collected, including equipment failure rate, outage time, fault type, and repair time. Load data of power grid equipment is also collected, including load data of each node of the distribution network, especially load fluctuations during peak hours and under special circumstances. The status of power grid equipment is then monitored, including the operating status and health status of equipment such as transformers, switches, and lines. Step S2. Reliability Model Establishment Select an appropriate assessment method based on the assessment objectives, set the failure rate, maintenance time, and load level parameters for each component based on the collected data, determine the network topology of the distribution network, and identify key equipment and load nodes. Step S3. Evaluation and Analysis Using the selected evaluation methods, various reliability indicators of the distribution network are calculated, such as SAIDI (system average outage time), SAIFI (system average outage frequency), and CAIDI (customer average outage duration). Through Monte Carlo simulation, the system operation under different fault scenarios and load conditions is simulated to evaluate the reliability of the system under different conditions, analyze equipment or areas with high fault risk, predict possible outage events, and prepare emergency plans in advance. Step S4. Result Analysis and Optimization By assessing the results, weaknesses and potential risk areas in the distribution network are identified, key factors affecting reliability are found, and the design (such as adding backup equipment and optimizing load distribution) and operation and maintenance strategies (such as strengthening inspection and maintenance in high-failure-rate areas) of the distribution network are optimized based on the assessment results. Targeted improvement measures are then implemented to reduce the occurrence of faults and improve the stability and fault resistance of the distribution network. Step S5. Regular evaluation and updates Regular reliability assessments should be conducted on power grid equipment to ensure that the distribution network continuously meets the requirements for safe, stable and reliable power supply. Step S6. Feedback on Evaluation Results Based on the assessment results, the design, operation, and maintenance strategies of the distribution network should be adjusted in a timely manner to ensure that the system is in optimal condition in the long term.
[0026] In this invention, the reliability assessment system plays a crucial role in the planning, design, operation, and maintenance of power systems. Through precise assessment methods, the reliability and stability of the distribution network can be ensured, thereby improving the overall safety and power supply quality of the power system.
[0027] In this invention, a comprehensive evaluation of the system is conducted through various methods. The goal is to identify potential fault points, optimize the operation of the distribution network, and improve the stability and reliability of power supply. Through the system evaluation methods and processes, the power system can better cope with possible faults and emergencies, thereby ensuring that customers' electricity needs are reliably guaranteed.
[0028] The following points should be noted in this article: 1. The accompanying drawings of the embodiments disclosed herein only relate to the structures involved in the embodiments disclosed herein; other structures can be referred to in a general design.
[0029] 2. Where there is no conflict, the embodiments of this disclosure and the features in the embodiments can be combined with each other to obtain new embodiments.
[0030] Although embodiments of the present invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions, and variations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without inventive effort are within the scope of protection of the present invention.
Claims
1. A reliability assessment system for a medium-voltage distribution network structure, characterized in that, It includes a data acquisition and monitoring module, a fault diagnosis module, an evaluation model module, a simulation and optimization module, a decision support module, and a reporting and evaluation module; Data acquisition and monitoring module: By installing various sensors and monitoring equipment, it monitors various operating parameters in the power grid in real time, including current, voltage, and power, to collect key data such as current, voltage, and load in the distribution network; Fault diagnosis module: It uses fault diagnosis technology and machine learning algorithms to predict possible equipment failures, take measures in advance to reduce power outages and power losses, and identify and locate fault points in the distribution network to help restore power supply quickly; The evaluation model module calculates the reliability index of the distribution network through statistical, probability theory and power system models, and analyzes the data to evaluate the reliability of each part of the distribution network and identify potential weak links in the system. The simulation and optimization module simulates the distribution network structure and analyzes its operation under different conditions to help optimize the power grid configuration and emergency plans. The decision support module provides support for grid dispatch, operation and maintenance, and upgrades based on the assessment results, and provides power companies with decision support for reliability improvement, including equipment maintenance, replacement, and optimized dispatch. The reporting and evaluation module generates reports based on evaluation results and proposes improvement plans to help power companies improve the reliability of the distribution network and reduce the risk of power outages.
2. The reliability assessment system for a medium-voltage distribution network structure according to claim 1, characterized in that, The data acquisition and monitoring module mainly collects historical fault data, including equipment failure rate, downtime, fault type, and repair time. It can also collect load data, including load data of each node in the distribution network, especially load fluctuations during peak hours and under special circumstances.
3. The reliability assessment system for a medium-voltage distribution network structure according to claim 1, characterized in that, The data acquisition and monitoring module can also monitor data status, including the operating status and health status of equipment such as transformers, switches, and lines.
4. The reliability assessment system for a medium-voltage distribution network structure according to claim 1, characterized in that, The specific implementation process of the fault diagnosis module is as follows: 1) By monitoring parameters such as current and voltage in real time, abnormal changes in waveforms can be analyzed to detect whether there is a equipment malfunction; 2) Utilize intelligent sensors to monitor the operating status of equipment in real time and detect abnormalities in electrical equipment at an early stage; 3) By applying pattern recognition, machine learning and data mining techniques, identify and classify fault modes; for example, analyze historical fault data based on algorithms such as support vector machines and decision trees to help locate the probability and severity of fault occurrence.
5. The reliability assessment system for a medium-voltage distribution network structure according to claim 1, characterized in that, The specific implementation process of the evaluation model module is as follows: 1) Simulate the operating state of the power grid by using the topology of the power grid, and analyze the load of each device and its impact on the overall reliability of the system; 2) By discretizing the power grid operating state, a mathematical model of the power grid state is established to simulate the impact of equipment failures, load changes, etc. on the reliability of the power grid; 3) Based on random sampling technology, the reliability of the distribution network can be evaluated by simulating a large number of possible operating scenarios, which can effectively handle the uncertainties and randomness in the system.
6. The reliability assessment system and method for a medium-voltage distribution network structure according to claim 1, characterized in that, The simulation and optimization module can also propose equipment replacement or maintenance suggestions based on equipment failure rate and repair time, optimize the power grid topology based on reliability analysis, reduce the impact of fault points on the overall reliability of the power grid, and suggest setting backup lines or equipment in critical locations to improve system redundancy and ensure that the power grid can recover quickly in the event of a fault.
7. The reliability assessment system for a medium-voltage distribution network structure according to claim 1, characterized in that, The reporting and assessment module includes the distribution network reliability status, optimization suggestions, and future reliability forecasts; The power distribution network reliability status is used to demonstrate the health status of the power grid, assess the reliability of equipment, and analyze potential risks. The optimization recommendations are used to propose specific suggestions for system optimization, equipment maintenance, and replacement based on the evaluation results; The future reliability forecast can predict future trends in power grid reliability based on existing data, helping power companies to develop countermeasures in advance.
8. A reliability assessment method for a medium-voltage distribution network structure, characterized in that, Includes the following steps: Step S1. Data Collection and Preprocessing Historical fault data of medium-voltage distribution network equipment is collected, including equipment failure rate, outage time, fault type, and repair time. Load data of power grid equipment is also collected, including load data of each node of the distribution network, especially load fluctuations during peak hours and under special circumstances. The status of power grid equipment is then monitored, including the operating status and health status of equipment such as transformers, switches, and lines. Step S2. Reliability Model Establishment Select an appropriate assessment method based on the assessment objectives, set the failure rate, maintenance time, and load level parameters for each component based on the collected data, determine the network topology of the distribution network, and identify key equipment and load nodes. Step S3. Evaluation and Analysis Using the selected evaluation method, calculate various reliability indicators of the distribution network. Through Monte Carlo simulation, simulate the system operation under different fault scenarios and load conditions, evaluate the reliability of the system under different conditions, analyze equipment or areas with high fault risk, predict possible power outage events, and prepare emergency plans in advance. Step S4. Result Analysis and Optimization By assessing the results, weaknesses and potential risk areas in the distribution network are identified, key factors affecting reliability are found, and the design and operation and maintenance strategies of the distribution network are optimized based on the assessment results. Targeted improvement measures are then implemented to reduce the occurrence of faults and improve the stability and fault resistance of the distribution network. Step S5. Regular evaluation and updates Regular reliability assessments should be conducted on power grid equipment to ensure that the distribution network continuously meets the requirements for safe, stable and reliable power supply. Step S6. Feedback on Evaluation Results Based on the assessment results, the design, operation, and maintenance strategies of the distribution network should be adjusted in a timely manner to ensure that the system is in optimal condition in the long term.