A Fault Prediction Method for Medium and Low Voltage Distribution Systems

Abstract

The present invention relates to a method for predicting faults in a medium and low voltage power distribution system, which belongs to the technical field of medium and low voltage power distribution systems, and includes the following steps: S1, state monitoring stage; S2, data collection and analysis; S3, feature extraction and model establishment; S4, fault diagnosis and prediction; S5, maintenance decision and system optimization; S6, safety precautions. The method for predicting faults in a medium and low voltage power distribution system can monitor and analyze the operating status of the power distribution system in real time and effectively identify early signs of potential faults by comprehensively using advanced sensor technology, big data analysis and machine learning algorithms; it significantly improves the accuracy and reliability of fault prediction, reduces the risk of power outages caused by false alarms or missed alarms, can realize the prediction and diagnosis of faults in medium and low voltage power distribution systems, improve the reliability and safety of the system, and achieve the advantages of high prediction accuracy and good early warning effect.

Description

Technical Field

[0001] The present invention relates to the technical field of medium and low voltage power distribution systems, and in particular to a method for predicting faults in medium and low voltage power distribution systems. Background Art

[0002] As an essential component of power supply, medium and low voltage distribution systems cover a wide range of areas, from regional substations to user meters. These systems typically face the following challenges: 1. Complexity and diversity: Medium and low voltage distribution systems involve a variety of distribution terminals, such as station terminals, feeder terminals, and distribution transformer terminals, which exchange data through wired and wireless networks. Due to the large number of distribution points and the wide range of areas involved, data transmission and monitoring become particularly complex; 2. Frequent failures and widespread impact: Due to equipment aging, environmental factors, and human operation, medium and low voltage distribution systems are prone to failures. These failures not only affect the stability of power supply, but may also cause damage to equipment and users; 3. Data silos and lack of integration: Although some regions have established systems such as dispatching automation, distribution automation, and metering automation, these systems often operate independently and lack data integration and sharing, resulting in limited fault prediction and diagnosis capabilities.

[0003] Currently, fault prediction and diagnosis of medium and low voltage distribution systems mainly rely on the following technologies: 1. Sensor monitoring: By installing sensors in the distribution network to measure parameters such as current and voltage, the data is monitored in real time to detect outliers. However, this method is limited by the layout and accuracy of the sensors and may not accurately capture all faults; 2. Statistical analysis: Statistical methods are used to detect outliers in sensor data to identify potential faults. However, this method has high requirements for data integrity and accuracy and has difficulty coping with complex and changing fault conditions; 3. Machine learning: By learning the normal operating patterns of the low voltage distribution network from historical data, and using these patterns to detect faults. However, due to the complexity and diversity of medium and low voltage distribution systems, the optimization and training of machine learning algorithms have become particularly difficult.

[0004] In the prior art, the invention patent with announcement number CN103368149B discloses a method for monitoring faults in a low-voltage power distribution system, which includes the following steps: monitoring the low-voltage output switch; obtaining the status information of the low-voltage output switch when the current is too low alarm; and determining the fault type of the low-voltage power distribution system based on the status information of the low-voltage output switch. In the above-mentioned method for monitoring faults in a low-voltage power distribution system, the status of the low-voltage output switch is understood in real time by monitoring the low-voltage output switch. When the system issues a low-current alarm, the type of fault is quickly determined based on the status information of the low-voltage output switch, which saves the time wasted by relevant technical personnel going to the production line for on-site inspection and reduces the losses caused by delays.

[0005] However, this method has problems such as low inspection frequency and limited analysis methods, and cannot meet the requirements of modern power systems for fault prediction accuracy and timeliness. In recent years, with the rapid development of technologies such as the Internet of Things, big data, and artificial intelligence, the fault prediction method of low-voltage distribution systems has been significantly improved. By real-time monitoring of the operating status of equipment, collecting and analyzing sensor data, and applying machine learning algorithms, it is possible to accurately predict and promptly eliminate faults in low-voltage distribution systems. In the future, more attention will be paid to in-depth analysis and mining of data, and more advanced machine learning algorithms and artificial intelligence technologies will be used to improve the accuracy and timeliness of fault prediction. In order to achieve intelligent prediction and management of low-voltage distribution system faults, this application proposes a low-voltage distribution system fault prediction method. Summary of the Invention

[0006] In response to the shortcomings of the existing technology, the present invention provides a method for predicting faults in medium and low voltage power distribution systems, which has the advantages of high prediction accuracy and good early warning effect. It solves the problem that the existing prediction methods have poor early warning effect, resulting in reduced accuracy in predicting faults in medium and low voltage power distribution systems.

[0007] To achieve the above object, the present invention provides the following technical solution: a method for predicting faults in a medium and low voltage power distribution system, comprising the following steps:

[0008] S1. Status Monitoring: This phase involves real-time or periodic monitoring of various parameters of the medium and low voltage power distribution systems to obtain information on the equipment's operating status. Using real-time online monitoring equipment, the location of equipment faults can be accurately determined without on-site inspection personnel, reducing labor troubleshooting costs.

[0009] S2. Data collection and analysis: Collect various data during the operation of medium and low voltage distribution systems, process and analyze the collected data, and extract useful information to reveal abnormal behavior or fault characteristics of equipment;

[0010] S3. Feature extraction and model building: Extract fault-related features from the analysis results. These features will be used to establish fault diagnosis and prediction models. Based on historical data and fault characteristics, a machine learning algorithm is used to establish a fault prediction model.

[0011] S4. Fault Diagnosis and Prediction: Utilize fault signatures and known fault patterns, employing pattern recognition, expert systems, and fuzzy logic to diagnose equipment faults and determine whether a fault has occurred, as well as the type and location of the fault. Based on established fault prediction models, predict the future state of medium and low voltage distribution systems, assess the likelihood of a fault, and determine the time and location of a possible fault.

[0012] S5. Maintenance Decision-Making and System Optimization: Develop reasonable maintenance strategies based on fault diagnosis and prediction results to ensure reliable equipment operation. Integrate fault diagnosis and prediction methods into a comprehensive system to achieve automated and intelligent fault management. Furthermore, based on actual application results, continuously optimize and improve fault diagnosis and prediction methods to enhance system accuracy and reliability.

[0013] S6. Safety precautions: When predicting faults in medium and low voltage power distribution systems, ensure safe operation and follow relevant safety regulations and standards. When touching electrical equipment, take appropriate protective measures to avoid dangerous situations such as electric shock and short circuit.

[0014] Furthermore, the parameters that need to be monitored in S1 include the temperature of the medium and low voltage power distribution system, the current of the medium and low voltage power distribution system, the voltage of the medium and low voltage power distribution system, and the vibration of the medium and low voltage power distribution system.

[0015] Furthermore, the status monitoring stage in S1 includes the following steps:

[0016] S1-1. Select monitoring parameters: First, you need to determine the parameters to be monitored, which will reflect the operating status and performance of the equipment;

[0017] S1-2. Install monitoring equipment: Select appropriate sensors based on the monitoring parameter requirements. The sensors are reasonably configured in key parts of the system to ensure that they can fully and accurately reflect the operating status of the system.

[0018] S1-3, Data Collection: The operation data of the medium and low voltage distribution system is collected in real time through sensors. After the collection is completed, the data is transmitted to the data processing center or monitoring system for further analysis and processing;

[0019] S1-4, Data processing: Process the collected raw data, eliminate noise and outliers in the data through data processing, and improve the accuracy and reliability of the data;

[0020] S1-5. Feature extraction and data analysis: Extract feature information related to medium and low voltage distribution system faults from the processed data. Analyze this feature information using statistical methods and machine learning algorithms to determine whether the system has potential fault risks.

[0021] S1-6, Status Assessment and Early Warning: Based on the results of feature extraction and data analysis, the status of the medium and low voltage distribution system is assessed. If the system status is abnormal or there is a potential failure risk, the early warning mechanism is triggered and an early warning message is issued to relevant personnel so that they can take timely measures to deal with it;

[0022] S1-7. Data storage and management: The collected data, processed data, feature extraction and data analysis results, and status assessment and early warning information are stored and managed. The stored data and information can be used for subsequent system optimization, troubleshooting, and maintenance decisions.

[0023] Furthermore, the sensors in S1-2 include a temperature sensor, a current sensor, a voltage sensor and a vibration sensor.

[0024] Furthermore, the data processing in S1-4 includes data cleaning, data filtering and data calibration.

[0025] Furthermore, the characteristic information in S1-5 includes current fluctuation, voltage anomaly and temperature rise.

[0026] Furthermore, the warning information in S1-6 includes detailed information on the fault type, fault location and fault severity.

[0027] Furthermore, the data collection in S2 includes real-time monitoring data and historical data; the data analysis in S2 includes statistical analysis, spectrum analysis and time domain analysis.

[0028] Furthermore, the machine learning algorithm in S3 is any one of support vector machine, neural network or random forest.

[0029] Furthermore, the maintenance strategy in S5 is any one or more of preventive maintenance, predictive maintenance or emergency maintenance measures.

[0030] Compared with the prior art, the present invention provides a method for predicting faults in medium and low voltage power distribution systems, which has the following beneficial effects:

[0031] 1. This fault prediction method for medium and low voltage distribution systems, through the comprehensive application of advanced sensor technology, big data analysis, and machine learning algorithms, can monitor and analyze the operating status of the distribution system in real time and effectively identify early signs of potential faults. It significantly improves the accuracy and reliability of fault prediction, reduces the risk of power outages due to false alarms or missed alarms, and can predict and diagnose medium and low voltage distribution system faults, improving system reliability and safety, achieving the advantages of high prediction accuracy and good early warning effect.

[0032] 2. This fault prediction method for medium and low voltage distribution systems can issue early warning signals in time before a fault occurs or in the early stages through real-time monitoring and prediction, providing valuable response time for operation and maintenance personnel. This helps operation and maintenance personnel quickly locate the fault point and take necessary preventive measures or emergency repairs, thereby shortening the fault recovery time and reducing the impact on users' electricity consumption. By accurately locating the source of the fault, unnecessary inspections and maintenance work are reduced, reducing operation and maintenance costs. At the same time, through data analysis, the operation strategy of the distribution system can be optimized, energy utilization efficiency can be improved, and operating costs can be further reduced.

[0033] 3. This fault prediction method for medium and low voltage distribution systems helps improve the overall stability and safety of medium and low voltage distribution systems by timely discovering and handling potential faults. It can not only reduce power outages caused by faults, but also reduce safety risks such as fire and electric shock caused by system faults, thereby ensuring the safety of personnel and property. By realizing real-time monitoring and intelligent prediction of the distribution system, it provides strong support for the dispatching, operation and maintenance of smart grids, helps promote the digital transformation and intelligent upgrading of the power industry, and promotes the sustainable development of the power industry. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] Figure 1 A prediction flow chart of a method for predicting faults in a medium and low voltage power distribution system according to the present invention;

[0035] Figure 2 The present invention provides a monitoring flow chart for the state monitoring phase of a method for predicting faults in a medium and low voltage power distribution system. DETAILED DESCRIPTION

[0036] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the embodiments of the present invention and the accompanying drawings. Obviously, the described embodiments are only 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 making creative efforts are within the scope of protection of the present invention.

[0037] See also Figure 1 In this embodiment, a method for predicting faults in a medium and low voltage power distribution system includes the following steps:

[0038] S1. Status Monitoring: This phase involves real-time or periodic monitoring of various parameters of the medium and low voltage power distribution systems to obtain information on equipment operating status. This data helps analyze equipment health and identify potential faults. Using real-time online monitoring equipment, the location of equipment faults can be accurately determined without on-site inspection personnel, reducing labor-intensive troubleshooting costs.

[0039] S2. Data collection and analysis: Collect various data during the operation of medium and low voltage distribution systems, process and analyze the collected data, and extract useful information to reveal abnormal behavior or fault characteristics of equipment;

[0040] S3. Feature Extraction and Model Building: Extract fault-related features from the analysis results. These features will be used to diagnose and establish a prediction model. Based on historical data and fault characteristics, a machine learning algorithm is used to build a fault prediction model. The fault prediction model can predict the probability and time of future failures based on the current state of the equipment.

[0041] S4. Fault Diagnosis and Prediction: Utilize fault signatures and known fault patterns, employing pattern recognition, expert systems, and fuzzy logic to diagnose equipment faults and determine whether a fault has occurred, as well as the type and location of the fault. Based on established fault prediction models, predict the future state of medium and low voltage distribution systems, assess the likelihood of a fault, and determine the time and location of a possible fault.

[0042] S5. Maintenance Decision-Making and System Optimization: Develop reasonable maintenance strategies based on fault diagnosis and prediction results to ensure reliable equipment operation. Integrate fault diagnosis and prediction methods into a comprehensive system to achieve automated and intelligent fault management. Furthermore, based on actual application results, continuously optimize and improve fault diagnosis and prediction methods to enhance system accuracy and reliability.

[0043] S6. Safety precautions: When predicting faults in medium and low voltage power distribution systems, ensure safe operation and follow relevant safety regulations and standards. When touching electrical equipment, take appropriate protective measures to avoid dangerous situations such as electric shock and short circuit.

[0044] In this embodiment, by comprehensively applying advanced sensor technology, big data analysis, and machine learning algorithms, the operating status of the distribution system can be monitored and analyzed in real time, and early signs of potential faults can be effectively identified; the accuracy and reliability of fault prediction are significantly improved, and the risk of power interruptions due to false alarms or missed alarms is reduced. It can realize the prediction and diagnosis of medium and low voltage distribution system faults, improve the reliability and safety of the system, and achieve the advantages of high prediction accuracy and good early warning effect.

[0045] Specifically, the parameters that need to be monitored in S1 include the temperature of the medium and low voltage power distribution system, the current of the medium and low voltage power distribution system, the voltage of the medium and low voltage power distribution system, and the vibration of the medium and low voltage power distribution system.

[0046] Specifically, data collection in S2 includes real-time monitoring data and historical data; data analysis in S2 includes statistical analysis, spectrum analysis and time domain analysis.

[0047] Specifically, the machine learning algorithm in S3 is any one of support vector machine, neural network or random forest.

[0048] Specifically, the maintenance strategy in S5 is any one or more of preventive maintenance, predictive maintenance or emergency maintenance measures.

[0049] See also Figure 2 In this embodiment, the status monitoring stage in S1 includes the following steps:

[0050] S1-1. Select monitoring parameters: First, you need to determine the parameters to be monitored, which will reflect the operating status and performance of the equipment.

[0051] S1-2. Install monitoring equipment: Select appropriate sensors based on the monitoring parameter requirements. The sensors are reasonably configured in key parts of the system based on the monitoring requirements to ensure that they can fully and accurately reflect the operating status of the system.

[0052] S1-3. Data collection: The operating data of the medium and low voltage distribution systems are collected in real time through sensors. After the collection is completed, the data is transmitted to the data processing center or monitoring system for further analysis and processing.

[0053] S1-4. Data processing: Process the collected raw data, eliminate noise and outliers in the data through data processing, and improve the accuracy and reliability of the data.

[0054] S1-5. Feature extraction and data analysis: Extract feature information related to medium and low voltage distribution system faults from the processed data, and analyze this feature information using statistical methods and machine learning algorithms to determine whether the system has potential fault risks.

[0055] S1-6. Status assessment and early warning: Based on the results of feature extraction and data analysis, the status of the medium and low voltage distribution system is assessed. If the system status is abnormal or there is a potential failure risk, the early warning mechanism is triggered and early warning information is sent to relevant personnel so that they can take timely measures to deal with it.

[0056] S1-7. Data storage and management: The collected data, processed data, feature extraction and data analysis results, and status assessment and early warning information are stored and managed. The stored data and information can be used for subsequent system optimization, troubleshooting, and maintenance decisions.

[0057] Specifically, the sensors in S1-2 include a temperature sensor, a current sensor, a voltage sensor, and a vibration sensor.

[0058] Specifically, the data processing in S1-4 includes data cleaning, data filtering and data calibration.

[0059] Specifically, the characteristic information in S1-5 includes current fluctuation, voltage anomaly and temperature increase.

[0060] Specifically, the warning information in S1-6 includes detailed information on the fault type, fault location, and fault severity.

[0061] In this embodiment, the data processing in S1-4 includes the following steps:

[0062] S1-4-1. Data cleaning: Detect missing values ​​in the data set and select appropriate processing methods based on data characteristics and analysis requirements. Use statistical methods or visualization techniques to identify outliers in the data, and then decide whether to remove, correct, or retain these outliers. Ensure consistency in data format, units, naming, etc., and resolve data conflicts and inconsistencies. Identify and delete duplicate data by comparing record similarities or unique identifiers to maintain the uniqueness of the data set.

[0063] S1-4-2. Data filtering: By sampling at different time points within a cycle and then calculating the average value, periodic interference can be eliminated; the variable values ​​collected for several cycles are sorted, and then the middle value of the sorted values ​​is taken to effectively prevent the entry of data interfered by sudden pulses, and the low-pass filter allows low-frequency signals to pass through while filtering out high-frequency interference signals.

[0064] S1-4-3. Data calibration: clarify the calibration requirements, select appropriate calibration methods and standard equipment, and ensure the orderly progress of the calibration process; calibrate the data collector item by item according to the calibration plan and procedure documents, and prepare a detailed calibration report based on the calibration results. After the calibration is completed, the data collector should be verified and tested to confirm whether the calibration effect meets the expectations; the verification test should simulate the actual working environment to ensure that the data collector can work stably and accurately under real conditions; regularly clean, inspect and maintain the data collector to detect and eliminate potential faults in a timely manner.

[0065] Specifically, the data calibration in S1-4-3 includes information such as measurement values ​​before and after calibration, calibration time, and calibration personnel.

[0066] Specifically, the calibration report in S1-4-3 includes the calibration object, calibration method, environmental conditions, calibration data, calibration conclusions and suggestions, etc., providing a basis for the use and maintenance of the equipment.

[0067] The working principle of the present invention is:

[0068] This method collects real-time operating data of medium and low voltage distribution systems, uses advanced algorithms to analyze and process it, and accurately predicts potential faults. It provides timely early warning information to operation and maintenance personnel so that they can take appropriate preventive measures to ensure the stable operation of the power system.

[0069] While embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions, and variations may be made to these embodiments without departing from the principles and spirit of the invention, and that the scope of the invention is defined by the appended claims and their equivalents.

Claims

1. A method for predicting faults in medium and low voltage power distribution systems, characterized in that: The following steps are involved: S1. Status monitoring stage: Real-time or regular monitoring of various parameters of medium and low voltage power distribution systems to obtain information on equipment operating status; By using real-time online monitoring equipment, the location of equipment failure can be accurately determined without the need for on-site inspection personnel, thus reducing the cost of manpower troubleshooting. S2. Data collection and analysis: Collect various data during the operation of medium and low voltage distribution systems, process and analyze the collected data, and extract useful information to reveal abnormal behavior or fault characteristics of equipment; S3. Feature extraction and model building: Extract the features related to the fault from the analysis results. The features will be used for fault diagnosis and the establishment of prediction models. Based on historical data and fault characteristics, a machine learning algorithm is used to establish a fault prediction model. S4. Fault diagnosis and prediction: Using fault characteristics and known fault modes, pattern recognition, expert system and fuzzy logic methods are used to diagnose equipment faults, determine whether the equipment has a fault, and the type and location of the fault; based on the established fault prediction model, predict the future state of the medium and low voltage distribution system, evaluate the possibility of a fault, and determine the time and location where the fault may occur; S5. Maintenance decision-making and system optimization: Based on the fault diagnosis and prediction results, formulate reasonable maintenance strategies to ensure the reliable operation of equipment; integrate fault diagnosis and prediction methods into a comprehensive system to achieve automated and intelligent fault management; at the same time, continuously optimize and improve fault diagnosis and prediction methods based on actual application results to improve the accuracy and reliability of the system; S6. Safety precautions: When predicting faults in medium and low voltage power distribution systems, ensure safe operation and follow relevant safety regulations and standards. When contacting electrical equipment, take appropriate protective measures to avoid dangerous situations such as electric shock and short circuit.

2. A method for predicting faults in a medium and low voltage power distribution system according to claim 1, characterized in that: The parameters that need to be monitored in S1 include the temperature of the medium and low voltage power distribution system, the current of the medium and low voltage power distribution system, the voltage of the medium and low voltage power distribution system, and the vibration of the medium and low voltage power distribution system.

3. A method for predicting faults in a medium and low voltage power distribution system according to claim 1, characterized in that: The state monitoring stage in S1 includes the following steps: S1-1. Select monitoring parameters: First, you need to determine the parameters to be monitored, which reflect the operating status and performance of the equipment; S1-2. Install monitoring equipment: Select appropriate sensors according to the monitoring parameter requirements. The sensors are reasonably configured in key parts of the system according to the monitoring requirements to ensure that they can fully and accurately reflect the operating status of the system; S1-3, data collection: collect the operation data of the medium and low voltage distribution system in real time through sensors, and transmit the data to the data processing center or monitoring system for further analysis and processing after the collection is completed; S1-4, Data processing: Process the collected raw data, eliminate noise and outliers in the data through data processing, and improve the accuracy and reliability of the data; S1-5. Feature extraction and data analysis: Extract feature information related to medium and low voltage power distribution system faults from the processed data, and use statistical methods and machine learning algorithms to analyze these feature information to determine whether there is a potential fault risk in the system; S1-6, Status assessment and early warning: Based on the results of feature extraction and data analysis, the status of the medium and low voltage distribution system is assessed. If the system status is abnormal or there is a potential risk of failure, the early warning mechanism is triggered to send early warning information to relevant personnel so that they can take timely measures to deal with it; S1-7. Data storage and management: The collected data, processed data, feature extraction and data analysis results, and status assessment and early warning information are stored and managed. The stored data and information can be used for subsequent system optimization, troubleshooting, and maintenance decisions.

4. A method for predicting faults in a medium and low voltage power distribution system according to claim 1, characterized in that: The sensors in S1 - 2 include a temperature sensor, a current sensor, a voltage sensor and a vibration sensor.

5. A method for predicting faults in a medium and low voltage power distribution system according to claim 1, characterized in that: The data processing in S1-4 includes data cleaning, data filtering and data calibration.

6. A method for predicting faults in a medium and low voltage power distribution system according to claim 1, characterized in that: The characteristic information in S1-5 includes current fluctuation, voltage anomaly and temperature rise.

7. A method for predicting faults in a medium and low voltage power distribution system according to claim 1, characterized in that: The warning information in S1-6 includes detailed information on the fault type, fault location and fault severity.

8. A method for predicting faults in a medium and low voltage power distribution system according to claim 1, characterized in that: The data collection in S2 includes real-time monitoring data and historical data; the data analysis in S2 includes statistical analysis, spectrum analysis and time domain analysis.

9. A method for predicting faults in a medium and low voltage power distribution system according to claim 1, characterized in that: The machine learning algorithm in S3 is any one of a support vector machine, a neural network or a random forest.

10. A method for predicting faults in a medium and low voltage power distribution system according to claim 1, characterized in that: The maintenance strategy in S5 is any one or more of preventive maintenance, predictive maintenance or emergency maintenance measures.

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