Power grid maintenance risk early warning system

Abstract

The invention relates to a power grid maintenance risk early warning system in the technical field of power grid maintenance. The power grid maintenance risk early warning system comprises data collection equipment, data analysis equipment and a database, the risk early warning method of the power grid maintenance risk early warning system comprises the following steps: step 1, collecting power grid fault information, power grid fault field information and weather information of a power grid fault field predicted maintenance time period by using data collection equipment; 2, making possible risks in the first prediction maintenance process by using data analysis equipment according to the power grid fault information, the power grid fault field information and the weather information of the predicted maintenance time period and referring to historical data of a database, carrying out classification and grade evaluation on the risks, and making an operation guide and a protection suggestion; according to the invention, the safety in the power grid maintenance process is improved, the maintenance efficiency is enhanced, and real-time risk monitoring and response and optimized information recording and utilization are realized.

Description

Technical Field

[0001] The present invention relates to the technical field of power grid maintenance, and in particular to a risk early warning system for power grid maintenance. Background Art

[0002] Power grid maintenance is an important task that runs through all links of power generation, power supply, transformation, and distribution. Power grid maintenance can be divided into planned maintenance and fault maintenance according to types.

[0003] With the increasing scale and complexity of the power system, faults are inevitable. During power grid maintenance, risk control is required, and any negligence may become a potential safety hazard. The current precautions for power grid maintenance lack a unified risk early warning system and rely solely on the memory of maintenance personnel. Moreover, for sudden faults that may occur during maintenance, maintenance personnel need to respond temporarily according to regulations, which cannot guarantee the safety during power grid maintenance (especially during outdoor maintenance operations).

[0004] Therefore, we have designed a risk early warning system for power grid maintenance. Summary of the Invention

[0005] In order to overcome the deficiencies in the background art, the present invention discloses a risk early warning system for power grid maintenance.

[0006] To achieve the above-mentioned invention purpose, the present invention adopts the following technical solutions: A risk early warning system for power grid maintenance includes a data collection device, a data analysis device, and a database; the risk early warning method of the risk early warning system for power grid maintenance includes the following steps: Step 1: Use the data collection device to collect power grid fault information, power grid fault site information, and weather information during the expected maintenance time period at the power grid fault site; Step 2: Use the data analysis device to make a first prediction of the possible risks during the maintenance process based on the power grid fault information, power grid fault site information, and weather information during the expected maintenance time period, and refer to the historical data in the database, classify and evaluate the level of the risk, and make an operation guide and protection suggestions; Step 3: Use the data collection device to collect the fault maintenance site information and weather information during the maintenance process in real time; Step 4: Use the data analysis device to make a second prediction of the possible risks during the maintenance process based on the fault maintenance site information and weather information collected in real time, and refer to the historical data in the database, and give a temporary operation guide; Step 5: Save the fault maintenance site information, weather information, the risks predicted for the second time, and the temporary operation guide during the maintenance process to the database.

[0007] Preferably, in step four, after using the data analysis device to compare the risks predicted for the second time with those predicted for the first time and deleting the same risks, classify and evaluate the risks remaining after the second prediction.

[0008] Preferably, the data collection device includes: A sensor and monitoring system for real-time monitoring of the power grid status and collection of power grid fault information; A monitoring device for collecting images and video materials of the power grid fault site; A weather station access module for collecting weather information during the expected maintenance period at the power grid fault site; A portable weather station for real-time collection of weather information at the fault repair site.

[0009] Preferably, the data analysis device includes: A model construction module: constructing a risk prediction model based on machine learning, extracting features from image information using a convolutional neural network, and classifying and evaluating risks using a random forest algorithm; A warning module: used to warn about the risks predicted for the second time and the temporary operation guidelines.

[0010] Preferably, the warning module includes: A voice module for broadcasting the temporary operation guidelines by voice; A display module for displaying the temporary operation guidelines in text or video.

[0011] Due to the adoption of the above-mentioned technical solution, the present invention has the following beneficial effects: 1. Improve the safety during the power grid maintenance process: By predicting in advance the possible risks during the maintenance process and providing operation guidelines and protection suggestions, it helps to reduce potential safety hazards.

[0012] 2. Enhance the maintenance efficiency: The risks predicted for the first time and their corresponding operation guidelines and protection suggestions can help to reasonably allocate the number of staff and prepare all the required tools before the maintenance, reducing the time for unnecessary back-and-forth to get tools.

[0013] 3. Achieve real-time risk monitoring and response: Use the real-time collected data for the second risk prediction and give temporary operation guidelines to ensure that new situations or emergencies occurring during the maintenance can be promptly addressed, guaranteeing the smooth progress of the maintenance work.

[0014] 4. Optimize information recording and utilization: Save the information obtained during the maintenance process, the predicted risks, and the temporary operation guidelines to the database, providing a reference basis for subsequent maintenance work. Description of the Drawings

[0015] Figure 1 is a structural schematic block diagram of the present invention; Figure 2 is a flowchart of the risk warning method of the present invention.

[0016] In the figure: 100, data collection device; 110, sensor and monitoring system; 120, monitoring device; 130, weather station access module; 140, portable weather station; 200, data analysis device; 210, model construction module; 220, warning module; 221, voice module; 222, display module; 300, database. Specific embodiments

[0017] The present invention can be explained in detail through the following embodiments. The purpose of disclosing the present invention is to protect all technical improvements within the scope of the present invention. In the description of the present invention, it should be understood that if there are terms such as "upper", "lower", "front", "rear", "left", "right", etc. indicating the orientation or positional relationship, it is only corresponding to the drawings of the present application for the convenience of describing the present invention; it should be understood that if there are terms such as "end", "side", "end part", "side part", "lateral", "longitudinal", etc. indicating the orientation or positional relationship, it is only corresponding to the length and width of the corresponding component, that is, the "end part" indicates the head and tail regions in the length direction of the corresponding component, and the "side part" indicates the head and tail regions in the width direction of the corresponding component; it is for the convenience of describing the present invention rather than indicating or implying that the device or element referred to must have a specific orientation.

[0018] Combined with the attached Figure 1-2 , a power grid maintenance risk warning system includes a data collection device 100, a data analysis device 200, and a database 300, wherein: The data collection device 100 includes: The sensor and monitoring system 110 is used to monitor the power grid status (such as voltage, current, temperature, etc.) in real time and obtain power grid fault information when a power grid fault occurs; The monitoring device 120 is used to take photos or videos of the power grid fault site before and during maintenance to obtain power grid fault site information; as needed, when there is a camera near the power grid fault site, the monitoring device 120 can be a camera pre-installed at the power grid fault site; when there is no camera near the power grid fault site, the monitoring device 120 can be a drone or a mobile monitoring device 120 for temporary scheduling to take photos or videos; The weather station access module 130 is connected to a meteorological satellite and is used to collect and obtain the weather information of the power grid fault site during the expected maintenance period, and the weather information includes temperature, humidity, wind speed, and whether there is rain or snow, etc.

[0019] A portable weather station 140 is used for real-time monitoring of the weather at the power grid fault site during the maintenance process, collecting weather information during the maintenance process, and the weather information includes temperature, humidity, wind speed, etc.

[0020] The data analysis device 200 includes: A model construction module 210, which integrates algorithms such as convolutional neural network and random forest, constructs a risk prediction model based on machine learning. In the risk prediction model, the convolutional neural network is used to extract features from image information, and the random forest algorithm is used to classify and evaluate the level of risk.

[0021] A warning module 220: Communicatively connected to the model construction module 210, and is used to warn about the risks predicted by the risk prediction model and the operation guidelines based on the risks.

[0022] Preferably, in order to warn the operators in a timely and clear manner, the warning module 220 includes a voice module 221 and a display module 222. Among them, the voice module 221 can be a speaker carried by the staff, and the display module 222 can be a display screen; according to needs, the voice module 221 and the display module 222 are integrated on the same physical device.

[0023] The risk warning method of the power grid maintenance risk warning system includes the following steps: Step 1: Use the data collection device 100 to collect power grid fault information, power grid fault site information, and weather information during the expected maintenance period at the power grid fault site; Step 2: Use the data analysis device 200 to make a first prediction of the possible risks during the maintenance process based on the power grid fault information, power grid fault site information, and weather information during the expected maintenance period, and refer to the historical data in the database 300, classify and evaluate the level of the risk, and make operation guidelines and protection suggestions; The existence of this step can determine the number of staff with reasonable configuration according to the risks predicted for the first time, as well as the operation guidelines and protection suggestions made, and memorize it before the maintenance, and prepare the required tools according to the operation guidelines and protection suggestions, ensuring that all tools can be taken with only one visit to the fault site, reducing the operation of running back and forth to pick up tools and improving the maintenance efficiency.

[0024] Step 3: Use the data collection device 100 to collect the fault maintenance site information and weather information during the maintenance process in real time; Step 4: Use the data analysis device 200 to make a second prediction of the possible risks during the maintenance process based on the fault maintenance site information and weather information collected in real time, and refer to the historical data in the database 300, and give temporary operation guidelines; It should be noted that: Since there are basically no large deviations in whether it is sunny, rainy, snowy, foggy or temperature in the weather prediction information of the prior art, in this step, only the wind speed of the weather information needs to be collected in real time, and the wind speed includes flow velocity and direction.

[0025] The existence of this step can predict the possible risks in real time during the maintenance and make a temporary operation guide to ensure the smooth progress of the maintenance work.

[0026] Step Five: Save the fault repair site information, weather information, risks predicted for the second time, and the temporary operation guide during the maintenance process to the database 300 for reference during later maintenance operations.

[0027] Specifically, the warning module 220 is used to warn of the risks predicted for the second time and the temporary operation guide; more specifically, the voice module 221 is used to announce the temporary operation guide through voice broadcast; the display module 222 is used to display the temporary operation guide through text or video.

[0028] To simplify the content of the temporary operation guide and enable the operators to better receive the temporary operation guide, the data analysis device 200 is used to compare the risks predicted for the second time with the risks predicted for the first time, delete the same risks, and then classify and evaluate the remaining risks predicted for the second time.

[0029] The parts not detailed in the present invention are the prior art. For those skilled in the art, it is obvious that the present invention is not limited to the details of the above exemplary embodiments, and can be implemented in other specific forms without departing from the spirit or basic characteristics of the present invention; therefore, from any point of view, the embodiments should be regarded as exemplary and non-restrictive, aiming to include all changes falling within the meaning and scope of the equivalent elements in the present invention.

Claims

1. A power grid maintenance risk early warning system, characterized by: Including data collection equipment, data analysis equipment and database; The risk warning method of the power grid maintenance risk warning system comprises the following steps: Step 1: Use data collection equipment to collect power grid fault information, power grid fault site information, and weather information during the expected maintenance period at the power grid fault site; Step 2: Use data analysis equipment to make the first prediction of the risks that may occur during the maintenance process based on the power grid fault information, power grid fault site information and weather information during the expected maintenance period, and refer to the historical data in the database, and classify and grade the risks, as well as make operation guidelines and protection suggestions; Step 3: Use data collection equipment to collect fault repair site information and weather information in real time during the repair process; Step 4: Use data analysis equipment to make a second prediction of the risks that may occur during the maintenance process based on the real-time collected fault maintenance site information and weather information, and refer to the historical data in the database, and provide temporary operation guidelines; Step 5: Save the fault repair site information, weather information, second predicted risks, and temporary operation guidelines during the maintenance process to the database.

2. The power grid maintenance risk early warning system according to claim 1 is characterized by: In step four, the risks predicted for the second time are compared with those predicted for the first time using data analysis equipment, and after identical risks are deleted, the remaining risks predicted for the second time are classified and graded.

3. The power grid maintenance risk early warning system according to claim 1 is characterized by: The data collection device comprises: Sensors and monitoring systems are used to monitor the status of the power grid in real time and collect information on power grid faults; Monitoring equipment to collect images and video data from power grid fault sites; The weather station access module is used to collect weather information during the expected maintenance period at the power grid fault site; Portable weather station, used to collect weather information at the fault repair site in real time.

4. The power grid maintenance risk early warning system according to claim 1 is characterized by: The data analysis device comprises: Model building module: Build a risk prediction model based on machine learning, use convolutional neural networks to extract features from image information, and use random forest algorithms to classify and grade risks; Warning module: used to warn of the risks of the second prediction and temporary operation guidelines.

5. The power grid maintenance risk early warning system according to claim 4 is characterized in that: The warning module comprises: Voice module, used for providing temporary operation instructions by voice broadcast; The display module is used for displaying temporary operation instructions through text or video.

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