Operation fault monitoring and predicting system of power distribution system

Abstract

The invention relates to the technical field of power distribution systems, in particular to a power distribution system operation fault monitoring and predicting system which comprises a data acquisition module used for acquiring operation data of a power distribution system in real time; the data processing module is used for preprocessing, storing and transmitting the data acquired by the data acquisition module; the fault detection and prediction module is used for monitoring the operation state of the power distribution system in real time, detecting abnormal conditions and predicting potential faults; the user interface is used for man-machine interaction and displaying real-time data, historical data, alarm information and maintenance suggestions; the maintenance management module is used for making a management maintenance plan, recording maintenance information and evaluating a maintenance effect; early detection and prediction of faults are realized, and reliability and operation efficiency of a power distribution system are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of power distribution systems, and in particular to a power distribution system operation fault monitoring and prediction system. Background Art

[0002] The distribution system is an important part of the power system, and its operating status directly affects the stability and reliability of power supply. Traditional distribution system monitoring methods mainly rely on manual inspections and regular maintenance, which have problems such as slow response and low efficiency. With the development of industrial Internet and big data technology, it is possible to achieve real-time monitoring and fault prediction of distribution systems through advanced sensing technology and data analysis methods. However, existing fault monitoring systems often lack efficient fault prediction capabilities and real-time performance. For example, a distribution system operation fault monitoring and prediction system disclosed in announcement number: CN209927955U includes a data acquisition unit, a data transmission unit, a power protection unit, a personal computer terminal, and a mobile phone terminal, which improves the efficiency of fault inspection and repair of distribution stations, ensures personnel safety, and reduces property losses; but it cannot meet the requirements of modern distribution systems for high reliability and high efficiency. Summary of the invention

[0003] The technical problem to be solved by the present invention is to overcome the deficiencies of the prior art and provide a distribution system operation fault monitoring and prediction system.

[0004] The technical solution adopted by the present invention to solve the technical problem is: the distribution system operation fault monitoring and prediction system includes

[0005] Data acquisition module: used to collect the operation data of the power distribution system in real time;

[0006] Data processing module: used for preprocessing, storing and transmitting the data collected by the data acquisition module;

[0007] Fault detection and prediction module: used to monitor the operating status of the power distribution system in real time, detect abnormal conditions, and predict potential faults;

[0008] User interface: used for human-computer interaction, displaying real-time data, historical data, alarm information, maintenance suggestions, etc.;

[0009] Maintenance management module: used to formulate maintenance management plans, record maintenance information, and evaluate maintenance effects.

[0010] The data acquisition module includes a plurality of sensors.

[0011] The sensor includes

[0012] Current sensor: used to monitor current changes in the circuit;

[0013] Voltage sensors, used to monitor voltage changes in a circuit;

[0014] Temperature sensors, used to monitor the temperature of equipment and lines;

[0015] Vibration sensors, used to monitor the vibration of equipment;

[0016] Environmental sensors are used to monitor environmental parameters such as humidity, temperature, etc.

[0017] The data processing module includes

[0018] Data cleaning unit: used to remove invalid, erroneous or missing data;

[0019] Data standardization unit: used to convert data into a unified format and unit;

[0020] Data compression unit: used to reduce the burden of data transmission and storage;

[0021] Database: used to implement data storage.

[0022] The database includes

[0023] Real-time database: used to store real-time data and support real-time monitoring;

[0024] Historical database: used to store historical data and support long-term analysis and trend forecasting.

[0025] This application uses a real-time database and a historical database to store real-time data and historical data respectively, supporting real-time monitoring and long-term analysis.

[0026] The fault detection and prediction module includes

[0027] Condition monitoring unit: used to monitor current, voltage, temperature, vibration and other parameter information in real time to ensure that the system operates within the normal range;

[0028] Abnormal detection unit: used to detect abnormal conditions such as sudden current change, excessive temperature, abnormal vibration, etc. by setting thresholds and rules;

[0029] Feature extraction unit: used to extract key features from historical data, such as mean, variance, spectrum, etc.;

[0030] Machine learning model unit: used to train the fault prediction model using a supervised learning algorithm; the machine learning model module uses a supervised learning algorithm, including but not limited to random forest, support vector machine, neural network, etc., to train the fault prediction model.

[0031] Model evaluation unit: used to evaluate the performance of the model through cross-validation and performance indicators;

[0032] Early warning system unit: used to set early warning rules according to the results of the fault prediction model, and notify relevant personnel through SMS, email, APP push, etc.

[0033] The user interface comprises:

[0034] Real-time data display unit: used to display current current, voltage, temperature, vibration and other parameter information;

[0035] Historical data chart unit: used to display trend charts and statistical charts of historical data;

[0036] Alarm information unit: used to display current alarm information and historical alarm records;

[0037] Maintenance suggestion unit: used to generate maintenance plans and suggest maintenance time and content based on fault prediction results.

[0038] The maintenance management module includes:

[0039] Maintenance plan management unit: used to formulate detailed maintenance plans, track the implementation of maintenance plans, and dynamically adjust maintenance plans based on fault prediction results and historical maintenance records;

[0040] Maintenance record management unit: used to save detailed records of each maintenance, including maintenance time, maintenance content, maintenance personnel, etc., and provide maintenance record query function;

[0041] Maintenance effect evaluation unit: used to evaluate the effect of each maintenance, including changes in failure rate, stability of system operation, etc., and propose improvement measures based on the evaluation results to optimize maintenance plans and methods.

[0042] It also includes edge computing devices for preliminary data processing and filtering, reducing the amount of data transmission and improving system response speed.

[0043] The system ensures that the time synchronization accuracy of all nodes reaches nanosecond level through the Precision Time Protocol (PTP), thereby ensuring the accuracy of data collection.

[0044] Compared with the prior art, the present invention has the following beneficial effects:

[0045] The present invention realizes early detection and prediction of faults and improves the reliability and operation efficiency of the power distribution system by real-time collection and analysis of the operation data of the power distribution system.

[0046] It can monitor the operating status of the power distribution system in real time, detect abnormal situations in time, and reduce fault response time; it monitors multiple parameters such as current, voltage, temperature, vibration, etc. through a variety of sensors to ensure the comprehensiveness and accuracy of monitoring.

[0047] Through real-time monitoring and fault prediction, the system can detect and handle faults in a timely manner, reducing power outages caused by faults; through early warning and timely maintenance, the system can extend the service life of equipment and improve the overall reliability of the system; reducing power outages and improving power supply stability can enhance customer satisfaction and trust.

[0048] It can predict potential failures, issue early warnings, and avoid the occurrence of failures; use historical data and feature extraction technology to improve the accuracy and reliability of failure prediction; by predicting failures, it can reasonably arrange maintenance resources, avoid unnecessary maintenance work, and reduce maintenance costs. DETAILED DESCRIPTION

[0049] The technical solutions in the embodiments of the present invention are described clearly and completely below. 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 creative work are within the scope of protection of the present invention.

[0050] The distribution system operation fault monitoring and prediction system includes

[0051] Data acquisition module: used to collect the operation data of the power distribution system in real time;

[0052] The data acquisition module includes a plurality of sensors.

[0053] The sensor includes

[0054] Current sensor: used to monitor current changes in the circuit;

[0055] Voltage sensors, used to monitor voltage changes in a circuit;

[0056] Temperature sensors, used to monitor the temperature of equipment and lines;

[0057] Vibration sensors, used to monitor the vibration of equipment;

[0058] Environmental sensors are used to monitor environmental parameters such as humidity, temperature, etc.

[0059] Data processing module: used to pre-process, store and transmit the data collected by the data acquisition module; the data processing module includes

[0060] Data cleaning unit: used to remove invalid, erroneous or missing data;

[0061] Data standardization unit: used to convert data into a unified format and unit;

[0062] Data compression unit: used to reduce the burden of data transmission and storage;

[0063] Database: used to implement data storage.

[0064] The database includes

[0065] Real-time database: used to store real-time data and support real-time monitoring;

[0066] Historical database: used to store historical data and support long-term analysis and trend forecasting.

[0067] This application uses a real-time database and a historical database to store real-time data and historical data respectively, supporting real-time monitoring and long-term analysis.

[0068] Fault detection and prediction module: used to monitor the operating status of the power distribution system in real time, detect abnormal conditions, and predict potential faults; the fault detection and prediction module includes

[0069] Condition monitoring unit: used to monitor current, voltage, temperature, vibration and other parameter information in real time to ensure that the system operates within the normal range;

[0070] Abnormal detection unit: used to detect abnormal conditions such as sudden current change, excessive temperature, abnormal vibration, etc. by setting thresholds and rules;

[0071] Feature extraction unit: used to extract key features from historical data, such as mean, variance, spectrum, etc.;

[0072] Machine learning model unit: used to train the fault prediction model using a supervised learning algorithm; the machine learning model module uses a supervised learning algorithm, including but not limited to random forest, support vector machine, neural network, etc., to train the fault prediction model.

[0073] Model evaluation unit: used to evaluate the performance of the model through cross-validation and performance indicators;

[0074] Early warning system unit: used to set early warning rules according to the results of the fault prediction model, and notify relevant personnel through SMS, email, APP push, etc.

[0075] User interface: used for human-computer interaction, displaying real-time data, historical data, alarm information and maintenance suggestions, etc.; the user interface includes:

[0076] Real-time data display unit: used to display current current, voltage, temperature, vibration and other parameter information;

[0077] Historical data chart unit: used to display trend charts and statistical charts of historical data;

[0078] Alarm information unit: used to display current alarm information and historical alarm records;

[0079] Maintenance suggestion unit: used to generate maintenance plans and suggest maintenance time and content based on fault prediction results.

[0080] Maintenance management module: used to formulate maintenance management plans, record maintenance information, and evaluate maintenance effects.

[0081] The maintenance management module includes:

[0082] Maintenance plan management unit: used to formulate detailed maintenance plans, track the implementation of maintenance plans, and dynamically adjust maintenance plans based on fault prediction results and historical maintenance records;

[0083] Maintenance record management unit: used to save detailed records of each maintenance, including maintenance time, maintenance content, maintenance personnel, etc., and provide maintenance record query function;

[0084] Maintenance effect evaluation unit: used to evaluate the effect of each maintenance, including changes in failure rate, stability of system operation, etc., and propose improvement measures based on the evaluation results to optimize maintenance plans and methods.

[0085] It also includes edge computing devices for preliminary data processing and filtering, reducing the amount of data transmission and improving system response speed.

[0086] The system ensures that the time synchronization accuracy of all nodes reaches nanosecond level through the Precision Time Protocol (PTP), thereby ensuring the accuracy of data collection.

[0087] The above descriptions are only optional embodiments of the present invention, and are not intended to limit the patent scope of the present invention. All equivalent structural changes made using the contents of the present invention specification under the inventive concept of the present invention, or direct / indirect applications in other related technical fields are included in the patent protection scope of the present invention.

Claims

1. A distribution system operation fault monitoring and prediction system, characterized in that: include Data acquisition module: used to collect the operation data of the power distribution system in real time; Data processing module: used for preprocessing, storing and transmitting the data collected by the data acquisition module; Fault detection and prediction module: used to monitor the operating status of the power distribution system in real time, detect abnormal conditions, and predict potential faults; User interface: used for human-computer interaction, displaying real-time data, historical data, alarm information and maintenance suggestions; Maintenance management module: used to formulate maintenance management plans, record maintenance information, and evaluate maintenance effects.

2. The power distribution system operation fault monitoring and prediction system according to claim 1, characterized in that: The data acquisition module includes a plurality of sensors.

3. The power distribution system operation fault monitoring and prediction system according to claim 2, characterized in that: The sensor includes Current sensor: used to monitor current changes in the circuit; Voltage sensors, used to monitor voltage changes in a circuit; Temperature sensors, used to monitor the temperature of equipment and lines; Vibration sensors, used to monitor the vibration of equipment; Environmental sensors are used to monitor environmental parameters.

4. The power distribution system operation fault monitoring and prediction system according to claim 1, characterized in that: The data processing module includes Data cleaning unit: used to remove invalid, erroneous or missing data; Data standardization unit: used to convert data into a unified format and unit; Data compression unit: used to reduce the burden of data transmission and storage; Database: used to implement data storage.

5. The power distribution system operation fault monitoring and prediction system according to claim 4, characterized in that: The database includes Real-time database: used to store real-time data and support real-time monitoring; Historical database: used to store historical data and support long-term analysis and trend forecasting.

6. The power distribution system operation fault monitoring and prediction system according to claim 4, characterized in that: The fault detection and prediction module includes Condition monitoring unit: used to monitor parameter information in real time to ensure that the system operates within the normal range; Anomaly detection unit: used to detect anomalies by setting thresholds and rules; Feature extraction unit: used to extract key features from historical data; Machine learning model unit: used to train fault prediction models using supervised learning algorithms; Model evaluation unit: used to evaluate the performance of the model through cross-validation and performance indicators; Early warning system unit: used to set early warning rules based on the results of the fault prediction model.

7. The power distribution system operation fault monitoring and prediction system according to claim 1, characterized in that: The user interface comprises: Real-time data display unit: used to display current parameter information; Historical data chart unit: used to display trend charts and statistical charts of historical data; Alarm information unit: used to display current alarm information and historical alarm records; Maintenance suggestion unit: used to generate maintenance plans and suggest maintenance time and content based on fault prediction results.

8. The power distribution system operation fault monitoring and prediction system according to claim 1, characterized in that: The maintenance management module includes: Maintenance plan management unit: used to formulate maintenance plans, track the implementation of maintenance plans, and dynamically adjust maintenance plans based on fault prediction results and historical maintenance records; Maintenance record management unit: used to save detailed records of each maintenance, including maintenance time, maintenance content, maintenance personnel, and provide maintenance record query function; Maintenance effect evaluation unit: used to evaluate the effect of each maintenance, including changes in failure rate and stability of system operation, and propose improvement measures based on the evaluation results to optimize maintenance plans and methods.

9. The power distribution system operation fault monitoring and prediction system according to claim 1, characterized in that: It also includes edge computing devices for preliminary data processing and filtering, reducing the amount of data transmission and improving system response speed.