Fault diagnosis system and method based on state monitoring of power transformation and distribution equipment

Abstract

The invention provides a fault diagnosis system and method based on state monitoring of power transformation and distribution equipment. The fault diagnosis system comprises an indicator lamp state monitoring module, an instrument reading monitoring module, an oil temperature digital monitoring module, a temperature monitoring module and a terminal processing module. The terminal processing module is used for receiving the equipment indicator lamp state data, the instrument reading value, the converted oil temperature number and the temperature difference data, and when any one of the equipment indicator lamp state data, the instrument reading value, the converted oil temperature number and the temperature difference data does not meet the corresponding preset abnormity judgment requirement, the equipment indicator lamp state data is judged to be abnormal. And if so, sending an equipment operation abnormal warning to realize fault diagnosis of the power transformation and distribution equipment. According to the invention, by monitoring the equipment operation state in real time, the equipment operation abnormal state is found in time, and the equipment operation reliability is guaranteed.

Description

Technical Field

[0001] The present invention relates to the field of distribution substation monitoring, and particularly to a fault diagnosis system and method based on the status monitoring of power transformation and distribution equipment. Background Art

[0002] The distribution substation is used to provide power for the railway industry. Therefore, the requirements for the operation stability of the distribution substation are becoming more and more stringent. However, the traditional working mode of the distribution substation can no longer meet the railway operation requirements. How to timely detect the defects of the distribution substation equipment and improve the power supply guarantee of the distribution substation equipment is one of the means to improve the operation stability of the distribution substation at present;

[0003] At present, the prior art adopts the method of manual inspection, relying on organizing staff for periodic inspections. However, the traditional manual inspection method cannot timely detect and handle the hidden dangers of the distribution substation equipment, and there is still a lack of intelligent equipment monitoring. It can be seen that the prior art cannot intelligently identify the equipment status, resulting in the problem of poor reliability of equipment operation. Summary of the Invention

[0004] In order to solve the above problems, the present invention proposes a fault diagnosis system and method based on the status monitoring of power transformation and distribution equipment, which realizes timely detection of abnormal equipment operation status by real-time monitoring of the equipment operation status and ensures the reliability of equipment operation.

[0005] To achieve the above object, an embodiment of the present invention provides a fault diagnosis system based on the status monitoring of power transformation and distribution equipment, including: an indicator light status monitoring module, an instrument reading monitoring module, an oil temperature digital monitoring module, a temperature monitoring module, and a terminal processing module; the indicator light status monitoring module, the instrument reading monitoring module, the oil temperature digital monitoring module, and the temperature monitoring module are all electrically connected to the terminal processing module; the indicator light status monitoring module is used to identify the status of the equipment indicator light and send the equipment indicator light status data to the terminal processing module in real time; the instrument reading monitoring module is used to identify the instrument reading value and send the instrument reading value to the terminal processing module in real time; the oil temperature digital monitoring module is used to convert matchstick numbers into Arabic numerals and send the converted oil temperature numbers to the terminal processing module in real time; the temperature monitoring module is used to collect the equipment temperature, calculate the temperature difference data between the equipment temperature and a preset temperature reference point, and send the temperature difference data to the terminal processing module in real time; the terminal processing module is used to receive the equipment indicator light status data, the instrument reading value, the converted oil temperature number, and the temperature difference data. When any one of the equipment indicator light status data, the instrument reading value, the converted oil temperature number, and the temperature difference data does not meet the corresponding preset abnormal judgment requirement, an equipment operation abnormal warning is issued to realize the fault diagnosis of the power transformation and distribution equipment.

[0006] An embodiment of the present invention provides a fault diagnosis system based on the status monitoring of power transformation and distribution equipment. The system respectively monitors the status of equipment indicators, the readings of instruments, the oil temperature, and the equipment temperature through an indicator status monitoring module, an instrument reading monitoring module, an oil temperature digital monitoring module, and a temperature monitoring module, realizing intelligent monitoring of the equipment operation status from multiple perspectives, enabling comprehensive monitoring of the equipment in the distribution substation to ensure the stable operation of equipment power supply and distribution. Then, the monitoring data is transmitted to the terminal processing module in real time for abnormal judgment. When any abnormal data is detected, an abnormal warning is issued. By monitoring the abnormal monitoring of real-time monitoring data, potential safety hazards and abnormal operation conditions of equipment operation can be discovered in a timely manner. Thus, the workload of manual inspection is reduced through the intelligent monitoring system, the equipment operation status is monitored from multiple aspects, and the reliability of equipment operation is ensured by timely discovering abnormal operation status of the equipment.

[0007] Further, the indicator status monitoring module is used to identify the status of equipment indicators and transmit the equipment indicator status data to the terminal processing module in real time, including: an equipment indicator image acquisition unit, an equipment indicator area feature extraction unit, an equipment indicator status recognition model training unit, and an equipment indicator data transmission unit; the equipment indicator image acquisition unit is used to acquire the indicator images of all equipment in the distribution substation, and label the position, shape, color, and status of the indicator images through a preset annotation algorithm to obtain an indicator image database; the equipment indicator area feature extraction unit is used to extract features from the indicator images in the indicator image database through a preset neural network to obtain an indicator image feature data set; the equipment indicator status recognition model training unit is used to train a preset classification model according to the indicator image feature data set to obtain an equipment indicator status recognition model; the equipment indicator data transmission unit is used to obtain the equipment indicator status image in real time, perform status recognition on the equipment indicator status image through the equipment indicator status recognition model, output the equipment indicator status data, and transmit the equipment indicator status data to the terminal processing module in real time.

[0008] Through the above solution, the classification model is trained with a large amount of labeled data, enabling the model to learn the features in the indicator status image. Finally, the trained model is used to realize real-time monitoring of the indicator status. Through a large amount of feature learning, the accuracy of obtaining indicator status monitoring data can be ensured, and thus the reliability of equipment operation can be ensured.

[0009] Further, the instrument reading monitoring module is used to identify the instrument reading value and send the instrument reading value to the terminal processing module in real time, including: an instrument image acquisition and processing unit, a region model training unit, and an instrument reading value output unit; the instrument image acquisition and processing unit is used to acquire the instrument image according to the preset position, and perform region division on the instrument image through a preset segmentation algorithm, scale key points, and pointer key points to obtain a region division data set; the region model training unit is used to train a preset neural network model according to the region division data set to obtain a region prediction model; the instrument reading value output unit is used to identify the scale key points and pointer key points of the instrument image obtained in real time through the region prediction model, read the instrument reading value, and send the instrument reading value to the terminal processing module in real time.

[0010] Through the above solution, by focusing on the key regions of the image through region division and then training the neural network model, the model can accurately identify the scale key points and pointer key points to read the instrument reading value, thereby realizing the monitoring of the instrument and ensuring the reliability of the equipment operation.

[0011] Further, the oil temperature digital monitoring module is used to convert matchstick numbers into Arabic numbers and send the converted oil temperature numbers to the terminal processing module in real time, including: an oil temperature digital image acquisition unit, an oil temperature digital image feature extraction unit, an oil temperature digital conversion model training unit, and an oil temperature digital data transmission unit; the oil temperature digital image acquisition unit is used to acquire a number of matchstick oil temperature digital images from different sources, label the corresponding Arabic numbers for the number of matchstick oil temperature digital images to obtain an oil temperature digital image labeling data set; the oil temperature digital image feature extraction unit is used to extract the position, length, and direction features of the matchsticks in the oil temperature digital image from the oil temperature digital image labeling data set through a preset feature extraction algorithm to obtain an oil temperature digital image digital feature set; the oil temperature digital conversion model training unit is used to train a preset deep learning model according to the oil temperature digital image digital feature set to obtain an oil temperature digital conversion model; the oil temperature digital data transmission unit is used to convert the matchstick numbers in the matchstick oil temperature digital image into Arabic numbers in real time according to the oil temperature digital conversion model and send the converted oil temperature numbers to the terminal processing module in real time.

[0012] Through the above solution, by pre-labeling the Arabic numbers for the matchstick numbers from different sources, then extracting the corresponding matchstick number features, making the matchstick numbers and Arabic numbers correspond, and then training the deep learning model to learn the correspondence between the matchstick numbers and Arabic numbers, the conversion between the accurate matchstick numbers and Arabic numbers is used to realize the oil temperature monitoring and ensure the reliability of the equipment operation.

[0013] Furthermore, the temperature monitoring module is used to collect the device temperature, calculate the temperature difference data between the device temperature and the preset temperature reference point, and send the temperature difference data to the terminal processing module in real time, including: a temperature data acquisition unit, a temperature difference calculation unit, and a temperature data transmission unit; the temperature data acquisition unit is used to collect the device temperature data during operation, use the average value of the device temperature data as the temperature reference point, and collect the temperatures of corresponding parts between three-phase devices in the same group, between devices of the same phase, and between devices of the same type to obtain a temperature data set; the temperature difference calculation unit is used to calculate the difference between the temperature data in the temperature data set and the temperature reference point to obtain temperature difference data; the temperature data transmission unit is used to send the temperature difference data to the terminal processing module in real time.

[0014] Through the above solution, a temperature data set is obtained by comparing the temperatures of corresponding parts between three-phase devices in the same group, between devices of the same phase, and between devices of the same type, effectively removing the influence of the ambient temperature, reducing false alarms and misreports in temperature monitoring, and finally calculating the difference between the temperature value and the temperature reference value to obtain accurate temperature difference data, thereby ensuring the reliability of device operation.

[0015] Furthermore, the terminal processing module is used to receive device indicator light status data, instrument reading values, converted oil temperature numbers, and temperature difference data. When any one of the device indicator light status data, instrument reading values, converted oil temperature numbers, and temperature difference data does not meet the corresponding preset abnormal judgment requirements, an abnormal warning for device operation is issued to achieve fault diagnosis of the power transformation and distribution equipment, including: a data receiving unit, a device indicator light status abnormal judgment unit, an instrument reading abnormal judgment unit, a converted oil temperature number abnormal judgment unit, a temperature difference abnormal judgment unit, and an operation abnormal warning unit; the data receiving unit is used to receive device indicator light status data, instrument reading values, converted oil temperature numbers, and temperature difference data; the device indicator light status abnormal judgment unit is used to identify the status and color of the device indicator light through the device indicator light status data. When the status and color of the device indicator light do not meet the first abnormal judgment requirements, the abnormal judgment result is that the device is abnormal; the instrument reading abnormal judgment unit is used to obtain the status of the device switch and circuit breaker through the instrument reading value. When the status of the device switch and circuit breaker does not meet the second abnormal judgment requirements, the abnormal judgment result is that the device is abnormal; the converted oil temperature number abnormal judgment unit is used to determine that the abnormal judgment result is abnormal oil temperature when the converted oil temperature number does not meet the third abnormal judgment requirements; the temperature difference abnormal judgment unit is used to determine that the abnormal judgment result is abnormal temperature difference when the temperature difference data does not meet the fourth abnormal judgment requirements; the operation abnormal warning unit is used to issue an abnormal warning for device operation when any one of the corresponding abnormal judgment results of the device indicator light status abnormal judgment unit, the instrument reading abnormal judgment unit, the converted oil temperature number abnormal judgment unit, and the temperature difference abnormal judgment unit is abnormal, so as to achieve fault diagnosis of the power transformation and distribution equipment.

[0016] Through the above solution, by receiving device indicator light status data, instrument reading values, converted oil temperature numbers, and temperature difference data in real time, it is possible to identify the status and color of the device indicator light from the device indicator light status data and obtain the status of the device switch and circuit breaker from the instrument reading values. Finally, by judging whether the status and color of the device indicator light, the status of the device switch and circuit breaker, the oil temperature number, and the temperature data meet the corresponding abnormal judgment requirements, the monitoring of data anomalies is realized. When there is any abnormal result, an abnormal warning for device operation is issued. By judging the status of the device, device anomalies can be discovered in a timely manner to ensure the reliability of device operation.

[0017] Furthermore, a fault diagnosis system based on the status monitoring of power transformation and distribution equipment proposed in an embodiment of the present invention further includes: an intelligent patrol module; the intelligent patrol module is electrically connected to the terminal processing module; the intelligent patrol module is configured to perform equipment patrol tasks according to a custom patrol route, patrol time, and patrol cycle. When detecting equipment anomalies, it stores the anomaly equipment data and sends the anomaly equipment data to the terminal processing module. Specifically, the intelligent patrol module is configured to perform equipment patrol tasks according to a custom patrol route, patrol time, and patrol cycle. When detecting equipment anomalies, it stores the anomaly equipment data and sends the anomaly equipment data to the terminal processing module, including: a patrol task processing unit, a patrol data analysis unit, and an anomaly data transmission unit; the patrol task processing unit is configured to bind monitoring equipment on the patrol route according to the custom patrol route, and automatically perform equipment patrol tasks according to the patrol time and patrol cycle; the patrol data analysis unit is configured to capture images of each patrol target equipment and compare them with historical patrol record values to obtain patrol data analysis results; the anomaly data transmission unit is configured to when the patrol data analysis results do not meet the fifth anomaly judgment requirement, the anomaly judgment result is that the equipment is abnormal, and it stores the anomaly equipment data and sends the anomaly equipment data to the terminal processing module.

[0018] Through the above solution, in addition to real-time monitoring of equipment status, an embodiment of the present invention also proposes to perform intelligent patrol on equipment. Through a predefined route, time, and cycle, it replaces manual work to achieve intelligent patrol. After identifying abnormal data, it sends the abnormal data to the terminal processing module to timely report equipment anomalies, thereby ensuring the reliability of equipment operation.

[0019] An embodiment of the present invention also provides a fault diagnosis method based on the status monitoring of power transformation and distribution equipment, which is executed by the terminal processing module and includes: receiving equipment indicator status data processed by the indicator status monitoring module, instrument reading values processed by the instrument reading monitoring module, converted oil temperature numbers processed by the oil temperature digital monitoring module, and temperature difference data processed by the temperature monitoring module; obtaining corresponding anomaly judgment results based on the equipment indicator status data processed by the indicator status monitoring module, instrument reading values processed by the instrument reading monitoring module, converted oil temperature numbers processed by the oil temperature digital monitoring module, and temperature difference data processed by the temperature monitoring module; based on the corresponding anomaly judgment results, if any one of the results is abnormal, an equipment operation anomaly warning is issued to achieve fault diagnosis of power transformation and distribution equipment.

[0020] An embodiment of the present invention proposes a fault diagnosis method based on the status monitoring of power transformation and distribution equipment. By receiving in real time the equipment indicator status data processed by the indicator status monitoring module, the instrument reading values processed by the instrument reading monitoring module, the converted oil temperature digital values processed by the oil temperature digital monitoring module, and the temperature difference data processed by the temperature monitoring module, the intelligent monitoring of the equipment operation status is realized from multiple angles, enabling the comprehensive monitoring of the equipment in the distribution substation to ensure the stable operation of the equipment power supply and distribution. Then, the monitoring data is transmitted to the terminal processing module in real time for anomaly judgment. When any abnormal data is detected, an anomaly warning is issued. By monitoring the anomaly of the real-time monitoring data, potential safety hazards and abnormal operation conditions in the equipment operation can be discovered in a timely manner. Thus, the workload of manual inspection is reduced through the intelligent monitoring system, the equipment operation status is monitored from multiple aspects, and the reliability of the equipment operation is ensured by timely discovering the abnormal operation status of the equipment. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] Figure 1 Schematic diagram of the module structure of a fault diagnosis system based on the status monitoring of power transformation and distribution equipment provided by an embodiment of the present invention Figure 1 ;

[0022] Figure 2 Schematic diagram of the module structure of a fault diagnosis system based on the status monitoring of power transformation and distribution equipment provided by an embodiment of the present invention Figure 2 ;

[0023] Figure 3 Schematic diagram of the module structure of a fault diagnosis system based on the status monitoring of power transformation and distribution equipment provided by an embodiment of the present invention Figure 3 ;

[0024] Figure 4 Schematic diagram of the step flow of a fault diagnosis method based on the status monitoring of power transformation and distribution equipment provided by an embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0025] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0026] In the embodiments of the present invention, the overall system architecture where the fault diagnosis system based on the status monitoring of power transformation and distribution equipment adopts a hierarchical and partitioned distributed structure, which is composed of a master station platform deployed at the power supply dispatching center and a basic platform deployed locally at the distribution substation. Repeater terminals can be deployed in the power supply section and the power supply workshop. Information interaction can be carried out among the master station platform, the basic platform, and the repeater terminals. Among them, all data of the distribution substation are uniformly processed and stored in the master station platform of the power supply dispatching center. The basic platform locally at the distribution substation includes a fault diagnosis system based on the status monitoring of power transformation and distribution equipment. The fault diagnosis system based on the status monitoring of power transformation and distribution equipment is used to intelligently monitor the operation status of the distribution substation equipment. For further illustration, the embodiments of the present invention also propose the equipment installation sites in the distribution substation. Specifically, they include: the high-voltage room, the control room, and the transformer room;

[0027] First, there are two rows of cabinets placed in the high-voltage room, with 10 cabinets in each row, totaling 20 switchgear cabinets. Among them, 2 incoming line cabinets are under the management of the power supply bureau. For the remaining 18 high-voltage cabinets, it is necessary to monitor the switch signal status of the upper panel of the cabinet body and the equipment main switch and energy storage status of the middle panel of the cabinet body. There are cable joints at the lower side inside the cabinet body, and temperature monitoring is required (3 cable joints in each cabinet, totaling 54 temperature measurement monitoring points). The high-voltage room applies for an independent network channel, which can use the data network 6C VPN with a bandwidth of 20M, obtain the SCADA system point table and access the telemetry signal, and can trigger an alarm when a change occurs. The specific installation design can be: install 1 network infrared fixed camera on each side wall of the room to monitor the front and rear channels of the high-voltage cabinets for security and personnel operation activity traceability. Hang 3 network infrared spherical cameras in the middle of the two rows of high-voltage cabinets to monitor and identify the switch status signals of the upper and middle panels of the high-voltage cabinet body, as well as the security of the main channel in the high-voltage room and the appearance status of the cabinet body. Install wireless temperature sensors at the cable heads (3 cable joints in each cabinet) at the lower side inside the high-voltage cabinet to monitor the real-time temperature of 54 cable heads. Install 2 sets of SF6 monitoring devices at intervals of 3 meters along the root of the back wall of the two rows of high-voltage cabinets to monitor whether SF6 leaks in the high-voltage room, and it can be linked with the fan. Install 1 set of water immersion sensor at a height of about 40 cm in the cable trench of the high-voltage room for waterlogging warning in the distribution substation. Install 1 set of water immersion sensor 30 cm above the root of the wall in the high-voltage room for severe flood warning in the substation, warning that the equipment is about to be soaked and immediate power-off of the equipment and evacuation of personnel are required. Install 1 set of temperature and humidity sensor, 3 sets of air-conditioning remote controllers, and 1 set of smoke sensor in the high-voltage room to monitor the operating environment temperature and humidity of the high-voltage room equipment, and set up linkages in the system to monitor the operating environment of the equipment;

[0028] Secondly, a row of switch cabinets is placed in the control room, including battery cabinets, AC / DC cabinets, and remote monitoring cabinets. It is necessary to monitor the appearance of the batteries to check for phenomena such as smoking and leakage, and at the same time be able to monitor the signal status of the AC / DC cabinet panel and the operating status of the remote monitoring equipment. The specific installation design can be as follows: Install 1 infrared spherical camera on the front wall of the switch cabinet for monitoring the switch cabinet panel and the appearance of the batteries; install 1 infrared fixed camera on the back wall of the switch cabinet for security and tracing of personnel operation activities; install 1 set of temperature and humidity sensors in the control room, and 2 sets of air conditioner remote controllers to intelligently adjust and control the temperature and humidity environment; install 1 set of smoke sensors in the control room to monitor the control room and trigger system linkage and alarm in case of fire; install 1 set of face access control at the main entrance for personnel access management and video linkage, and the system can remotely open the door;

[0029] Finally, in the example of this embodiment, there are 2 transformer rooms in the distribution substation. It is necessary to monitor the appearance of the transformers and the values of the temperature control meters. The temperature control meter cycles through 4 groups of temperature data, and it is necessary to give an alarm when the temperature threshold is exceeded. The specific installation design can be as follows: Install one network spherical camera on the outer wall of the front guardrail of each transformer for monitoring the appearance of the transformers and the temperature control meters; install 1 set of smoke sensors in the transformer room to trigger system linkage and alarm in case of fire;

[0030] It is worth mentioning that in the embodiment of the present invention, network design is also particularly important. The transmission of data signals is a very important link, which directly affects the image clarity, latency, stuttering, video interruption, and control signal sending of the entire system. Therefore, all network devices in the distribution substation use private IP addresses to form a local area network at the distribution substation end. Then, through a three-layer switch, the IP addresses of the data management server and the video management server are mapped to the upper-level IP. Finally, the data is transmitted to the main station platform through the 6CVPN integrated data network. The main station platform accesses the local basic platform of the distribution substation through two IP addresses, which is convenient for IP management. The video and video data of the distribution substation can be stored locally. The main station platform and the repeater terminal can retrieve the video stream as needed. At the same time, important data and reports such as alarm information, intelligent inspection, and cable temperature can be uploaded to the main station platform in real time, which can effectively reduce the dependence of system data transmission on bandwidth.

[0031] Due to the disadvantages of traditional manual inspection, such as long inspection cycle, low inspection efficiency, low intelligence level, and high labor cost, in order to improve the reliability of equipment anomaly monitoring, the embodiment of the present invention proposes a fault diagnosis system based on the status monitoring of power transformation and distribution equipment. Refer to Figure 1 , Figure 1 which is a schematic module structure of a fault diagnosis system based on the status monitoring of power transformation and distribution equipment provided by an embodiment of the present invention Figure 1 。As Figure 1As shown in the figure, an embodiment of the present invention provides a fault diagnosis system based on the status monitoring of power transformation and distribution equipment, including: an indicator light status monitoring module 101, an instrument reading monitoring module 102, an oil temperature digital monitoring module 103, a temperature monitoring module 104, and a terminal processing module 105; the indicator light status monitoring module 101, the instrument reading monitoring module 102, the oil temperature digital monitoring module 103, and the temperature monitoring module 104 are all electrically connected to the terminal processing module 105;

[0032] The indicator light status monitoring module 101 is used to identify the status of the device indicator light and send the device indicator light status data to the terminal processing module 105 in real time;

[0033] As an example of the embodiment of the present invention, the indicator light status monitoring module 101 is used to identify the status of the device indicator light and send the device indicator light status data to the terminal processing module 105 in real time, including: a device indicator light image acquisition unit 201, a device indicator light area feature extraction unit 202, a device indicator light status recognition model training unit 203, and a device indicator light data transmission unit 204; the device indicator light image acquisition unit 201 is used to acquire the indicator light images of all devices in the substation, mark the positions, shapes, colors, and statuses of the indicator light images through a preset annotation algorithm to obtain an indicator light image database; the device indicator light area feature extraction unit 202 is used to extract features from the indicator light images in the indicator light image database through a preset neural network to obtain an indicator light image feature data set; the device indicator light status recognition model training unit 203 is used to train a preset classification model according to the indicator light image feature data set to obtain a device indicator light status recognition model; the device indicator light data transmission unit 204 is used to obtain the device indicator light status image in real time, perform status recognition on the device indicator light status image through the device indicator light status recognition model, output the device indicator light status data, and send the device indicator light status data to the terminal processing module 105 in real time.

[0034] In order to ensure the accuracy of obtaining the status monitoring data of the indicator lights and the reliability of the equipment operation, in the embodiments of the present invention, a classification model is trained with a large amount of labeled data, enabling the model to learn the features in the indicator light status images. Through a large amount of feature learning, the trained model is finally used to achieve real-time monitoring of the indicator light status. Specifically, in an implementable manner, images of the equipment indicator lights of all brands and models in the distribution substation are collected, and a triple guarantee mechanism of mine laying, automatic scoring, and review and proofreading is adopted to label all the equipment indicator light images. The labeling content includes: the position, shape, color, and status (on / off) of the indicator lights, and an indicator light image database is established; then, preprocessing operations such as grayscale conversion, denoising, and image enhancement are performed on the labeled images in the indicator light image database. Next, an adaptive histogram equalization algorithm is introduced to automatically adjust the contrast according to the image brightness, enhance the image details, and then a neural convolutional network is used to extract the image features of the indicator light area, including color, texture, and shape, etc. On this basis, an attention mechanism is introduced to improve the attention of the neural convolutional network model to the indicator light area, reduce background interference, and improve the accuracy of feature extraction. Finally, an indicator light image feature data set is obtained; after extracting a large number of indicator light area features, a classification model based on deep learning is constructed, and the classification model based on deep learning is trained with a large amount of feature data in the indicator light image feature data set. During the training process, data augmentation techniques (such as rotation, scaling, and flipping, etc.) can also be used to increase the diversity of the training set data and improve the generalization ability of the model. Finally, an equipment indicator light status recognition model is obtained. In order to further improve the accuracy of model recognition, regularization techniques and learning rate decay strategies can also be adopted to dynamically adjust the learning rate according to the loss change during the training process, effectively preventing the model from overfitting, accelerating the model convergence, optimizing the performance of the model, and improving the recognition accuracy. Finally, through the trained equipment indicator light status recognition model, the status of the equipment indicator lights is monitored and recognized in real time, and the recognized data is sent to the terminal processing module 105.

[0035] The instrument reading monitoring module 102 is used to identify the instrument reading value and send the instrument reading value to the terminal processing module 105 in real time;

[0036] As an example of an embodiment of the present invention, the instrument reading monitoring module 102 is used to identify the instrument reading value and send the instrument reading value to the terminal processing module 105 in real time, and includes: an instrument image acquisition and processing unit 301, a region model training unit 302, and an instrument reading value output unit 303; the instrument image acquisition and processing unit 301 is used to acquire an instrument image according to a preset position, and perform region division on the instrument image through a preset segmentation algorithm, scale key points, and pointer key points to obtain a region division data set; the region model training unit 302 is used to train a preset neural network model according to the region division data set to obtain a region prediction model; the instrument reading value output unit 303 is used to identify the scale key points and pointer key points of the instrument image obtained in real time through the region prediction model, read the instrument reading value, and send the instrument reading value to the terminal processing module 105 in real time.

[0037] In order to monitor instruments and meters and ensure the reliability of equipment operation, the embodiments of the present invention focus on key regions of images through region division, and then train a neural network model so that the model can accurately identify scale key points and pointer key points to read the reading values of instruments and meters. A specific implementable manner is to collect image information of switches, circuit breakers, and instruments and meters through a high-resolution camera with a pre-adjusted angle and preset positions. To ensure the clarity and accuracy of the images, a method of "turning on the lights before inspection and turning off the lights after inspection" is also proposed for intelligent night patrols without lights to improve the clarity and accuracy of the images, better reflect the actual position and state of the equipment. After the images are collected, preprocessing operations such as filtering, normalization, smoothing, and enhancement are performed on the images, and then an adaptive threshold segmentation algorithm is introduced to automatically adjust the threshold according to the image brightness and contrast to adapt to image recognition under different lighting conditions, eliminate noise and light interference, and thus partition the information of the standardized images. Region division is performed through scale key points and pointer key points to obtain a region division data set, and then the divided data set is used as a training sample to train the neural network model to obtain a region prediction model. The region prediction model is used to identify scale key points and pointer key points and perform region prediction. After completing the training of the region prediction model, through computer vision technology and image processing technology, such as template matching and feature point detection, the image to be recognized is obtained, and the gradient descent optimization algorithm is used to iteratively update the weights and biases of the region prediction model for the processed image to be recognized, so as to improve the accuracy of the model. Finally, the scale key points and pointer key points of the instruments and meters in the image to be recognized are identified through the region prediction model, and the reading values of the instruments and meters are calculated. It is worth mentioning that other key information, such as the position, shape, and color of the switch, can also be extracted from the image to be recognized through the above technical means to judge the state of the switch (such as on or off). Similarly, the state of the circuit breaker can also be judged through the same means. In addition, the reading values of the instruments and meters can also be associated and analyzed with the indicator light state to analyze and identify the operation state of the equipment from multiple dimensions; finally, the recognized reading values of the instruments and meters are sent to the terminal processing module 105.

[0038] The oil temperature digital monitoring module 103 is used to convert matchstick numbers into Arabic numbers and send the converted oil temperature numbers to the terminal processing module 105 in real time;

[0039] As an example of an embodiment of the present invention, the oil temperature digital monitoring module 103 is used to convert matchstick numbers into Arabic numerals and send the converted oil temperature numbers to the terminal processing module 105 in real time, including: an oil temperature digital image acquisition unit 401, an oil temperature digital image feature extraction unit 402, an oil temperature digital conversion model training unit 403, and an oil temperature digital data transmission unit 404; the oil temperature digital image acquisition unit 401 is used to acquire a plurality of matchstick oil temperature digital images from different sources, label the corresponding Arabic numerals for the plurality of matchstick oil temperature digital images, and obtain an oil temperature digital image labeling data set; the oil temperature digital image feature extraction unit 402 is used to extract the position, length, and direction features of the matchsticks in the oil temperature digital images from the oil temperature digital image labeling data set through a preset feature extraction algorithm, and obtain an oil temperature digital image digital feature set; the oil temperature digital conversion model training unit 403 is used to train a preset deep learning model according to the oil temperature digital image digital feature set to obtain an oil temperature digital conversion model; the oil temperature digital data transmission unit 404 is used to convert the matchstick numbers in the matchstick oil temperature digital images into Arabic numerals in real time according to the oil temperature digital conversion model, and send the converted oil temperature numbers to the terminal processing module 105 in real time.

[0040] Compared with the monitoring of the operating status of other devices, oil temperature monitoring is usually carried out in a complex background environment. In order to accurately identify matchstick numbers in a complex background, the matchstick numbers from different sources are pre-labeled with Arabic numerals, and then the corresponding matchstick number features are extracted so that the matchstick numbers and Arabic numerals can correspond. Then, a deep learning model is trained to learn the correspondence between matchstick numbers and Arabic numerals, and through this accurate conversion between matchstick numbers and Arabic numerals, oil temperature monitoring is realized to ensure the reliability of equipment operation. A specific implementable way is to collect matchstick number images covering various fonts, sizes, backgrounds, and lighting conditions, construct a matchstick number image dataset, and use a semi-automatic annotation tool and combine it with manual review to ensure that each matchstick number image can be accurately labeled with the corresponding Arabic numeral. After all matchstick images are labeled, an oil temperature digital image annotation dataset is obtained. Then, computer vision techniques such as edge detection and contour extraction are used to extract features such as the position, length, and direction of the matchsticks in each matchstick image in the oil temperature digital image annotation dataset, and an oil temperature digital image digital feature set (a matchstick number feature set related to Arabic numerals) is obtained. On this basis, by constructing a deep learning model and then using a large number of oil temperature digital image digital feature sets to train the deep learning model, the deep learning model is made to learn to classify matchstick images into corresponding Arabic numerals according to the digital features of the oil temperature digital images, and an oil temperature digital conversion model is obtained. To improve the accuracy of the model, the trained model can also be optimized by adjusting model parameters (such as learning rate, batch size, and regularization coefficient, etc.) and improving the network structure, etc., to improve the recognition accuracy. Finally, through the oil temperature digital conversion model, the real-time recognition and conversion of matchstick numbers are carried out, and the converted oil temperature numbers are sent to the terminal processing module 105. It is worth mentioning that in order to further improve the accuracy of oil temperature digital recognition, a feedback mechanism can also be introduced. When the recognition result is uncertain or ambiguous, manual correction or re-uploading of the picture can be carried out, and the feedback result is recorded to train and update the model, thereby improving the accuracy of model recognition and ensuring the reliability of equipment operation.

[0041] The temperature monitoring module 104 is used to collect the equipment temperature, calculate the temperature difference data between the equipment temperature and the preset temperature reference point, and send the temperature difference data to the terminal processing module 105 in real time;

[0042] As an example of an embodiment of the present invention, the temperature monitoring module 104 is used to collect the device temperature, calculate the temperature difference data between the device temperature and a preset temperature reference point, and send the temperature difference data to the terminal processing module 105 in real time, including: a temperature data acquisition unit 501, a temperature difference calculation unit 502, and a temperature data transmission unit 503; the temperature data acquisition unit 501 is used to collect the device temperature data during operation, use the average value of the device temperature data as the temperature reference point, and collect the temperatures of corresponding parts between the three-phase devices in the same group, between the devices of the same phase, and between the devices of the same type to obtain a temperature data set; the temperature difference calculation unit 502 is used to calculate the difference between the temperature data in the temperature data set and the temperature reference point to obtain temperature difference data; the temperature data transmission unit 503 is used to send the temperature difference data to the terminal processing module 105 in real time.

[0043] In order to obtain accurate temperature difference data and thus ensure the reliability of device operation, in the embodiment of the present invention, a temperature data set is obtained by comparing the temperatures of corresponding parts between the three-phase devices in the same group, between the devices of the same phase, and between the devices of the same type, effectively removing the influence of the ambient temperature, reducing false alarms and misreports in temperature monitoring, and finally calculating the difference between the temperature value and the temperature reference value. A specific implementable manner is as follows. First, according to the actual situation of the distribution substation equipment, select appropriate temperature measurement points for temperature monitoring, install high-precision infrared thermal imaging cameras at key parts of the distribution substation equipment, adjust the preset positions to collect the temperature data of the equipment in real time, and then select the average temperature during normal operation of the equipment or the temperature of a certain specific part as the reference to obtain the temperature reference point. In order to ensure that the temperature monitoring is not affected by the environment, a retest mechanism is introduced to increase the temperature collection of different parts of the same type of equipment to obtain a temperature data set, and multi-dimensional correlation analysis and comparison are performed. Specifically, the temperatures of corresponding parts between the three-phase devices in the same group, between the devices of the same phase, and between the devices of the same type are collected and analyzed. Use the maximum temperature value of the three-phase devices - the minimum temperature value = the temperature difference at the monitoring point. For example: collect the temperature of phase A, phase B, and phase C of a certain cable head. After comparison, the maximum value - the minimum value = the temperature difference of a certain cable head, and calculate the temperature difference between the temperature measurement point and the reference point to obtain the temperature difference data. The larger the temperature difference data, the higher the alarm level will be. Finally, the temperature difference data is sent to the terminal processing module 105.

[0044] The terminal processing module 105 is used to receive the device indicator light status data, the instrument reading value, the converted oil temperature digital value, and the temperature difference data. When any one of the device indicator light status data, the instrument reading value, the converted oil temperature digital value, and the temperature difference data does not meet the corresponding preset abnormal judgment requirements, an abnormal warning of device operation is issued to realize the fault diagnosis of the power transformation and distribution equipment.

[0045] As an example of an embodiment of the present invention, the terminal processing module 105 is configured to receive device indicator light status data, instrument reading values, converted oil temperature numbers, and temperature difference data. When any one of the device indicator light status data, instrument reading values, converted oil temperature numbers, and temperature difference data does not meet the corresponding preset abnormal judgment requirements, a device operation abnormal warning is issued to implement the fault diagnosis of the power transformation and distribution equipment, including: a data receiving unit 601, a device indicator light status abnormal judgment unit 602, an instrument reading abnormal judgment unit 603, a converted oil temperature number abnormal judgment unit 604, a temperature difference abnormal judgment unit 605, and an operation abnormal warning unit 606; the data receiving unit 601 is configured to receive device indicator light status data, instrument reading values, converted oil temperature numbers, and temperature difference data; the device indicator light status abnormal judgment unit 602 is configured to identify the status and color of the device indicator light through the device indicator light status data. When the status and color of the device indicator light do not meet the first abnormal judgment requirements, the abnormal judgment result is that the device is abnormal; the instrument reading abnormal judgment unit 603 is configured to obtain the status of the device switch and circuit breaker through the instrument reading value. When the status of the device switch and circuit breaker does not meet the second abnormal judgment requirements, the abnormal judgment result is that the device is abnormal; the converted oil temperature number abnormal judgment unit 604 is configured to when the converted oil temperature number does not meet the third abnormal judgment requirements, the abnormal judgment result is that the oil temperature is abnormal; the temperature difference abnormal judgment unit 605 is configured to when the temperature difference data does not meet the fourth abnormal judgment requirements, the abnormal judgment result is that the temperature difference is abnormal; the operation abnormal warning unit 606 is configured to when any one of the corresponding abnormal judgment results of the device indicator light status abnormal judgment unit 602, the instrument reading abnormal judgment unit 603, the converted oil temperature number abnormal judgment unit 604, and the temperature difference abnormal judgment unit 605 is abnormal, a device operation abnormal warning is issued to implement the fault diagnosis of the power transformation and distribution equipment.

[0046] At present, the existing technology has a long manual inspection cycle and low efficiency, and it is difficult to timely discover the potential safety hazards of the equipment. Therefore, the embodiment of the present invention can identify the status and color of the device indicator light from the device indicator light status data and obtain the status of the device switch and circuit breaker from the instrument reading value by receiving the device indicator light status data, instrument reading values, converted oil temperature numbers, and temperature difference data in real time. Finally, by judging whether the status and color of the device indicator light, the status of the device switch and circuit breaker, the oil temperature number, and the temperature data meet the corresponding abnormal judgment requirements, the monitoring of data anomalies is realized. When there is any abnormal result, a device operation abnormal warning is issued. By judging the status of the device, the device abnormality can be timely discovered to ensure the reliability of the device operation. For a specific implementable manner, see Figure 2 , Figure 2Schematic diagram of the module structure of a fault diagnosis system based on the status monitoring of power transformation and distribution equipment provided by an embodiment of the present invention Figure 2 , as shown in Figure 2 . The data receiving unit 601 receives the device indicator light status data processed by the indicator light status monitoring module 101, the instrument reading value processed by the instrument reading monitoring module 102, the converted oil temperature digital value processed by the oil temperature digital monitoring module 103, and the temperature difference data processed by the temperature monitoring module 104. The device indicator light status anomaly judgment unit 602, the instrument reading anomaly judgment unit 603, the converted oil temperature digital anomaly judgment unit 604, and the temperature difference anomaly judgment unit 605 respectively perform anomaly judgments on each data. When the device indicator light flashes or the color of the device indicator light changes from normal to abnormal (for example, green is normal and red is abnormal, and the color of the device indicator light changes from green to red), it indicates that the status and color of the device indicator light do not meet the first anomaly judgment requirement. At this time, the anomaly judgment result is judged as device anomaly; when it is recognized that a fault such as a short circuit occurs in the switch or the circuit breaker refuses to operate, it indicates that the status of the device switch and the circuit breaker does not meet the second anomaly judgment requirement, and the anomaly judgment result is device anomaly. When the oil temperature digital value is greater than the oil temperature threshold (such as 100 °C) or the fluctuation of the oil temperature digital value is greater than the hidden danger threshold (such as 5 °C), it indicates that the converted oil temperature digital value does not meet the third anomaly judgment requirement, and the anomaly judgment result is oil temperature anomaly. For the temperature difference data, it is compared with the preset temperature threshold. For example, the adjacent temperature comparison method and the same type temperature comparison method are used to implement the temperature difference alarm function. If the temperature difference between the temperature measurement point and the reference point is too high, too low, or exceeds a certain threshold, different levels of alarms are automatically triggered according to different differences. If the situation of being too high, too low, or exceeding the temperature threshold occurs, it indicates that the temperature difference data does not meet the fourth anomaly judgment requirement, and the anomaly judgment result is temperature difference anomaly; through the above anomaly judgments, when the operation anomaly alarm unit 606 monitors that any one of the anomaly judgment results is abnormal, it is necessary to report the abnormal situation and issue an alarm to achieve timely discovery of device anomalies and ensure the reliability of device operation.

[0047] As another example of the embodiment of the present invention, see Figure 3 , Figure 3 Schematic diagram of the module structure of a fault diagnosis system based on the status monitoring of power transformation and distribution equipment provided by an embodiment of the present invention Figure 3 , as shown in Figure 3As shown in the figure, an embodiment of the present invention provides a fault diagnosis system based on the status monitoring of power transformation and distribution equipment, further including: an intelligent inspection module 106; the intelligent inspection module 106 is electrically connected to the terminal processing module 105; the intelligent inspection module 106 is configured to perform equipment inspection tasks according to a customized inspection route, inspection time, and inspection cycle. When detecting equipment anomalies, it stores the abnormal equipment data and sends the abnormal equipment data to the terminal processing module 105. Specifically, the intelligent inspection module 106 is configured to perform equipment inspection tasks according to a customized inspection route, inspection time, and inspection cycle. When detecting equipment anomalies, it stores the abnormal equipment data and sends the abnormal equipment data to the terminal processing module 105, including: an inspection task processing unit 701, an inspection data analysis unit 702, and an abnormal data transmission unit 703; the inspection task processing unit 701 is configured to bind the monitoring equipment on the inspection route according to the customized inspection route, and automatically perform equipment inspection tasks according to the inspection time and inspection cycle; the inspection data analysis unit 702 is configured to capture images of each inspection target equipment and compare them with the historical inspection record values to obtain inspection data analysis results; the abnormal data transmission unit 703 is configured to, when the inspection data analysis results do not meet the fifth abnormal judgment requirement, the abnormal judgment result is that the equipment is abnormal, and store the abnormal equipment data and send the abnormal equipment data to the terminal processing module 105.

[0048] In addition to real-time monitoring of the device status, the embodiments of the present invention also propose to conduct intelligent inspections of the device. Through established routes, times, and cycles, manual inspections are replaced to achieve intelligent inspections. After abnormal data is identified, the abnormal data is sent to the terminal processing module 105 to promptly report the abnormal situation of the device, thereby ensuring the reliability of the device operation. In a specific implementable manner, the high-voltage room and control room devices are intelligently inspected 4 times a day. The intelligent inspection can customize the inspection route, inspection time, and inspection cycle. During the inspection process, images can be automatically captured and background image analysis can be performed. When an abnormal device is detected during the inspection, the key data of the abnormal device is stored, and then the terminal processing module 105 determines whether it is a device abnormality and automatically triggers an alarm. More specifically, according to the route, object, and inspection time cycle of the on-duty personnel's inspection, the camera preset positions, inspection paths, and inspection plans are customized. Images of the inspection targets are automatically captured and saved to achieve the intelligent inspection function. The monitoring devices participating in the intelligent device inspection can be arbitrarily selected, which can be different cameras or different preset positions of the same camera. The time interval for switching intelligent inspection video images can be customized, and the time cycle of the intelligent inspection can be customized. The intelligent inspection operation can sequentially inspect the monitoring ranges and monitoring targets of all cameras in the distribution substation according to a standard operation process for one inspection, and capture and save images of each inspection target. While capturing images, the intelligent recognition results of the device pictures are provided, and in the inspection results, by comparing with the historical record values of the inspection, after comparison, operation results such as normal or abnormal are provided. The intelligent device inspection cycle can be customized to be multiple times per day (week or month), greatly improving the inspection efficiency and bringing convenience to production work.

[0049] In addition, a fault diagnosis system based on the status monitoring of power transformation and distribution equipment proposed in the embodiments of the present invention can also set up an access control management system. By intelligently reading access control information, such as opening time, face, password, card identity, and door status, it is judged whether the opening permission can be satisfied. When opening the door, the camera can also be automatically associated and rotated to the position of the door for image capture and saving, thereby recording the access records and the identity information of the entering and leaving personnel, etc., to ensure the safety of the distribution substation. In addition, remote opening can be achieved through remote operation, and an access control abnormality warning can also be realized when multiple abnormal openings are recorded; a fault diagnosis system based on the status monitoring of power transformation and distribution equipment proposed in the embodiments of the present invention can also achieve environmental monitoring. By real-time monitoring of indoor temperature and humidity, water leakage, smoke, and SF6, etc., the real-time status and historical data of various sensors can be viewed in real time, and the alarm threshold can be set. When the real-time value exceeds the threshold, an alarm and video linkage are automatically triggered, and the historical values and historical curve and other report data can also be queried in the system.

[0050] An embodiment of the present invention provides a fault diagnosis system based on the status monitoring of power transformation and distribution equipment. The system can monitor the status of equipment indicators, the readings of instruments, the oil temperature, and the equipment temperature in real time through the indicator status monitoring module 101, the instrument reading monitoring module 102, the oil temperature digital monitoring module 103, and the temperature monitoring module 104 respectively. It realizes the intelligent monitoring of the equipment operation status from multiple perspectives, enables the comprehensive monitoring of the equipment in the distribution substation to ensure the stable operation of the equipment power supply and distribution, and then transmits the monitoring data to the terminal processing module 105 in real time for anomaly judgment. When any abnormal data is detected, an anomaly warning is issued. By monitoring the anomaly of the real-time monitoring data, potential safety hazards and abnormal operation conditions of the equipment operation can be discovered in time. Therefore, the workload of manual inspection is reduced through the intelligent monitoring system, the equipment operation status is monitored from multiple aspects, and the reliability of the equipment operation is ensured by timely discovering the abnormal operation status of the equipment.

[0051] Based on the above-mentioned fault diagnosis system based on the status monitoring of power transformation and distribution equipment, refer to Figure 4 , Figure 4 which is a schematic flow chart of the steps of a fault diagnosis method based on the status monitoring of power transformation and distribution equipment provided by an embodiment of the present invention. As Figure 4 shown, the embodiment of the present invention also provides a fault diagnosis method based on the status monitoring of power transformation and distribution equipment, including steps S1 to S3. The specific steps are as follows:

[0052] S1, receiving the equipment indicator status data processed by the indicator status monitoring module 101, the instrument reading value processed by the instrument reading monitoring module 102, the converted oil temperature digital processed by the oil temperature digital monitoring module 103, and the temperature difference data processed by the temperature monitoring module 104;

[0053] S2, obtaining the corresponding anomaly judgment result based on the equipment indicator status data processed by the indicator status monitoring module 101, the instrument reading value processed by the instrument reading monitoring module 102, the converted oil temperature digital processed by the oil temperature digital monitoring module 103, and the temperature difference data processed by the temperature monitoring module 104;

[0054] As an example of an embodiment of the present invention, the status and color of the device indicator light are identified through the status data of the device indicator light. When the status and color of the device indicator light do not meet the requirements of the first abnormal judgment, the abnormal judgment result is that the device is abnormal; the status of the device switch and circuit breaker is obtained through the reading value of the instrument. When the status of the device switch and circuit breaker does not meet the requirements of the second abnormal judgment, the abnormal judgment result is that the device is abnormal; when the converted oil temperature digital value does not meet the requirements of the third abnormal judgment, the abnormal judgment result is that the oil temperature is abnormal; when the temperature difference data does not meet the requirements of the fourth abnormal judgment, the abnormal judgment result is that the temperature difference is abnormal. For a specific explanation, when the status of the device indicator light flickers or the color of the device indicator light changes from normal to abnormal (for example, green is normal and red is abnormal, and the color of the device indicator light changes from green to red), it indicates that the status and color of the device indicator light do not meet the requirements of the first abnormal judgment. At this time, it is judged that the abnormal judgment result is that the device is abnormal; when it is recognized that there is a fault such as a short circuit in the switch or the circuit breaker fails to operate, it indicates that the status of the device switch and circuit breaker does not meet the requirements of the second abnormal judgment, and the abnormal judgment result is that the device is abnormal. When the oil temperature digital value is greater than the oil temperature threshold (such as 100 °C) or the fluctuation of the oil temperature digital value is greater than the hidden danger threshold (such as 5 °C), it indicates that the converted oil temperature digital value does not meet the requirements of the third abnormal judgment, and the abnormal judgment result is that the oil temperature is abnormal. For the temperature difference data, it is compared with the preset temperature threshold. For example, the adjacent temperature comparison method and the same type temperature comparison method are used to implement the temperature difference alarm function. If the temperature difference between the temperature measurement point and the reference point is too high, too low, or exceeds a certain threshold, different levels of alarms are automatically triggered according to different differences. If the situation of being too high, too low, or exceeding the temperature threshold occurs, it indicates that the temperature difference data does not meet the requirements of the fourth abnormal judgment, and the abnormal judgment result is that the temperature difference is abnormal.

[0055] S3. Based on the corresponding abnormal judgment result, if any one of the results is abnormal, a warning of abnormal device operation is issued to realize the fault diagnosis of the power transformation and distribution equipment.

[0056] An embodiment of the present invention provides a fault diagnosis method based on the status monitoring of power transformation and distribution equipment. By receiving in real time the equipment indicator status data processed by the indicator status monitoring module 101, the instrument reading values processed by the instrument reading monitoring module 102, the converted oil temperature digital values processed by the oil temperature digital monitoring module 103, and the temperature difference data processed by the temperature monitoring module 104, intelligent monitoring of the equipment operation status is achieved from multiple perspectives, enabling comprehensive monitoring of the equipment in the power distribution station to ensure stable power supply and distribution operation of the equipment. Then, the monitoring data is transmitted to the terminal processing module 105 in real time for anomaly judgment. When any abnormal data is detected, an anomaly warning is issued. By monitoring the real-time monitoring data for anomalies, potential safety hazards and abnormal operation conditions of the equipment operation can be detected in a timely manner. Thus, the workload of manual inspection is reduced through the intelligent monitoring system, the equipment operation status is monitored from multiple aspects, and the reliability of the equipment operation is ensured by timely detecting abnormal operation status of the equipment.

[0057] The above is only the preferred embodiment of the present invention. It should be noted that for those of ordinary skill in the art, without departing from the technical principle of the present invention, several improvements and modifications can be made, and these improvements and modifications should also be regarded as the protection scope of the present invention.

[0058] In the description of this specification, the description of reference terms such as "one embodiment", "some embodiments", "example", "specific example" or "some examples" means that the specific features, structures, materials or characteristics described in connection with the embodiment or example are included in at least one embodiment or example of the present application. Moreover, the specific features, structures, materials or characteristics described can be combined in a suitable manner in any one or more embodiments or examples. In addition, without contradiction, those skilled in the art can combine and combine the different embodiments or examples described in this specification and the features of different embodiments or examples.

[0059] In addition, the terms "first" and "second" are only used for descriptive purposes and cannot be construed as indicating or implying relative importance or implicitly indicating the quantity of the indicated technical features. Thus, the features defined with "first" and "second" may explicitly or implicitly include at least one of the features. In the description of the present application, "a plurality" means two or more, unless otherwise specifically defined.

Claims

1. A fault diagnosis system based on the status monitoring of power transformation and distribution equipment, characterized in that, Including: An indicator light status monitoring module, an instrument reading monitoring module, an oil temperature digital monitoring module, a temperature monitoring module, and a terminal processing module; The indicator light status monitoring module, the instrument reading monitoring module, the oil temperature digital monitoring module, and the temperature monitoring module are all electrically connected to the terminal processing module; The indicator light status monitoring module is used to identify the status of the device indicator light and send the device indicator light status data to the terminal processing module in real time; The instrument reading monitoring module is used to identify the instrument reading value and send the instrument reading value to the terminal processing module in real time; The oil temperature digital monitoring module is used to convert matchstick numbers into Arabic numbers and send the converted oil temperature numbers to the terminal processing module in real time; The temperature monitoring module is used to collect the device temperature, calculate the temperature difference data between the device temperature and the preset temperature reference point, and send the temperature difference data to the terminal processing module in real time; The terminal processing module is used to receive the device indicator light status data, the instrument reading value, the converted oil temperature number, and the temperature difference data. When any one of the device indicator light status data, the instrument reading value, the converted oil temperature number, and the temperature difference data does not meet the corresponding preset abnormal judgment requirements, an abnormal warning of device operation is issued to realize the fault diagnosis of the power transformation and distribution equipment.

2. The fault diagnosis system based on the status monitoring of power transformation and distribution equipment according to claim 1, characterized in that, The indicator light status monitoring module is used to identify the status of the device indicator light and send the device indicator light status data to the terminal processing module in real time, including: A device indicator light image acquisition unit, a device indicator light area feature extraction unit, a device indicator light status recognition model training unit, and a device indicator light data transmission unit; The device indicator light image acquisition unit is used to acquire the indicator light images of all devices in the substation, mark the position, shape, color, and status of the indicator light images through a preset annotation algorithm, and obtain an indicator light image database; The device indicator light area feature extraction unit is used to extract features from the indicator light images in the indicator light image database through a preset neural network to obtain an indicator light image feature data set; The device indicator light status recognition model training unit is used to train a preset classification model according to the indicator light image feature data set to obtain a device indicator light status recognition model; The device indicator light data transmission unit is used to obtain the device indicator light status image in real time, perform status recognition on the device indicator light status image through the device indicator light status recognition model, output the device indicator light status data, and send the device indicator light status data to the terminal processing module in real time.

3. A fault diagnosis system based on the status monitoring of power transformation and distribution equipment according to claim 1, wherein The instrument reading monitoring module is used to identify the instrument reading value and send the instrument reading value to the terminal processing module in real time, including: An instrument image acquisition and processing unit, a regional model training unit, and an instrument reading value output unit; The instrument image acquisition and processing unit is used to acquire instrument images according to the preset positions, divide the instrument images through a preset segmentation algorithm, scale key points, and pointer key points to obtain a regional division data set; The area model training unit is used to train a preset neural network model according to the area division data set to obtain an area prediction model; The instrument reading value output unit is used to identify the scale key points and pointer key points of the instrument image obtained in real time through the area prediction model, read the instrument reading value, and send the instrument reading value to the terminal processing module in real time.

4. A fault diagnosis system based on the status monitoring of power transformation and distribution equipment according to claim 1, characterized in that, The oil temperature digital monitoring module is used to convert matchstick numbers into Arabic numbers and send the converted oil temperature numbers to the terminal processing module in real time, including: An oil temperature digital image acquisition unit, an oil temperature digital image feature extraction unit, an oil temperature digital conversion model training unit, and an oil temperature digital data transmission unit; The oil temperature digital image acquisition unit is used to collect a number of matchstick oil temperature digital images from different sources, label the corresponding Arabic numbers for the number of matchstick oil temperature digital images, and obtain an oil temperature digital image labeling data set; The oil temperature digital image feature extraction unit is used to extract the position, length, and direction features of the matchsticks in the oil temperature digital image from the oil temperature digital image labeling data set through a preset feature extraction algorithm to obtain an oil temperature digital image digital feature set; The oil temperature digital conversion model training unit is used to train a preset deep learning model according to the oil temperature digital image digital feature set to obtain an oil temperature digital conversion model; The oil temperature digital data transmission unit is used to convert the matchstick numbers in the matchstick oil temperature digital image into Arabic numbers in real time according to the oil temperature digital conversion model, and send the converted oil temperature numbers to the terminal processing module in real time.

5. A fault diagnosis system based on the status monitoring of power transformation and distribution equipment according to claim 1, characterized in that, The temperature monitoring module is used to collect the equipment temperature, calculate the temperature difference data between the equipment temperature and the preset temperature reference point, and send the temperature difference data to the terminal processing module in real time, including: A temperature data acquisition unit, a temperature difference calculation unit, and a temperature data transmission unit; The temperature data acquisition unit is used to collect the equipment temperature data during operation, use the average value of the equipment temperature data as the temperature reference point, and collect the temperatures of the corresponding parts between the three-phase equipment in the same group, between the equipment of the same phase, and between the equipment of the same type to obtain a temperature data set; The temperature difference calculation unit is used to calculate the difference between the temperature data in the temperature data set and the temperature reference point to obtain temperature difference data; The temperature data transmission unit is used to send the temperature difference data to the terminal processing module in real time.

6. The fault diagnosis system based on the status monitoring of power transformation and distribution equipment according to claim 1, characterized in that The terminal processing module is used to receive the equipment indicator light status data, instrument reading value, converted oil temperature number, and temperature difference data. When any one of the equipment indicator light status data, instrument reading value, converted oil temperature number, and temperature difference data does not meet the corresponding preset abnormal judgment requirements, an equipment operation abnormal warning is issued to realize the fault diagnosis of the substation equipment, including: A data receiving unit, an equipment indicator light status abnormal judgment unit, an instrument reading abnormal judgment unit, a converted oil temperature number abnormal judgment unit, a temperature difference abnormal judgment unit, and an operation abnormal warning unit; The data receiving unit is used to receive device indicator light status data, instrument reading values, converted oil temperature numbers, and temperature difference data; The device indicator light status anomaly judgment unit is used to identify the status and color of the device indicator light through the device indicator light status data. When the status and color of the device indicator light do not meet the first anomaly judgment requirement, the anomaly judgment result is that the device is abnormal; The instrument reading anomaly judgment unit is used to obtain the device switch and circuit breaker status through the instrument reading values. When the device switch and circuit breaker status do not meet the second anomaly judgment requirement, the anomaly judgment result is that the device is abnormal; The converted oil temperature number anomaly judgment unit is used to determine that the anomaly judgment result is that the oil temperature is abnormal when the converted oil temperature number does not meet the third anomaly judgment requirement; The temperature difference anomaly judgment unit is used to determine that the anomaly judgment result is that the temperature difference is abnormal when the temperature difference data does not meet the fourth anomaly judgment requirement; The operation anomaly warning unit is used to issue a device operation anomaly warning when any one of the corresponding anomaly judgment results of the device indicator light status anomaly judgment unit, the instrument reading anomaly judgment unit, the converted oil temperature number anomaly judgment unit, and the temperature difference anomaly judgment unit is abnormal, so as to realize the fault diagnosis of the power transformation and distribution equipment.

7. A fault diagnosis system based on the status monitoring of power transformation and distribution equipment according to any one of claims 1 to 6, characterized in that, It further includes: An intelligent patrol module; The intelligent patrol module is electrically connected to the terminal processing module; The intelligent patrol module is used to execute the device patrol task according to the custom patrol route, patrol time, and patrol cycle. When a device anomaly is detected, it stores the anomaly device data and sends the anomaly device data to the terminal processing module.

8. The fault diagnosis system based on the status monitoring of power transformation and distribution equipment according to claim 7, characterized in that, The intelligent patrol module is used to execute the device patrol task according to the custom patrol route, patrol time, and patrol cycle. When a device anomaly is detected, it stores the anomaly device data and sends the anomaly device data to the terminal processing module, including: A patrol task processing unit, a patrol data analysis unit, and an anomaly data transmission unit; The patrol task processing unit is used to bind the monitoring devices on the patrol route according to the custom patrol route, and automatically execute the device patrol task according to the patrol time and patrol cycle; The patrol data analysis unit is used to capture images of each patrol target device and compare them with the historical patrol record values to obtain the patrol data analysis result; The anomaly data transmission unit is used to determine that the anomaly judgment result is that the device is abnormal when the patrol data analysis result does not meet the fifth anomaly judgment requirement, store the anomaly device data, and send the anomaly device data to the terminal processing module.

9. A fault diagnosis method based on the status monitoring of power transformation and distribution equipment, characterized in that, Applied to a fault diagnosis system based on power transformation and distribution equipment status monitoring according to any one of claims 1 to 8, executed by a terminal processing module, including: Receiving the device indicator light status data processed by the indicator light status monitoring module, the instrument reading values processed by the instrument reading monitoring module, the converted oil temperature numbers processed by the oil temperature number monitoring module, and the temperature difference data processed by the temperature monitoring module; Based on the device indicator light status data processed by the indicator light status monitoring module, the instrument reading value processed by the instrument reading monitoring module, the converted oil temperature digital value processed by the oil temperature digital monitoring module, and the temperature difference data processed by the temperature monitoring module, obtain the corresponding abnormal judgment result; Based on the corresponding abnormal judgment result, if any one of the results is abnormal, issue a warning of abnormal device operation to achieve fault diagnosis of the power transformation and distribution equipment.

10. A fault diagnosis method based on the status monitoring of power transformation and distribution equipment according to claim 9, characterized in that, Based on the device indicator light status data processed by the indicator light status monitoring module, the instrument reading value processed by the instrument reading monitoring module, the converted oil temperature digital value processed by the oil temperature digital monitoring module, and the temperature difference data processed by the temperature monitoring module, obtain the corresponding abnormal judgment result, including: Identify the status and color of the device indicator light through the device indicator light status data. When the status and color of the device indicator light do not meet the first abnormal judgment requirement, the abnormal judgment result is device abnormality; Obtain the status of the device switch and circuit breaker through the instrument reading value. When the status of the device switch and circuit breaker does not meet the second abnormal judgment requirement, the abnormal judgment result is device abnormality; When the converted oil temperature digital value does not meet the third abnormal judgment requirement, the abnormal judgment result is oil temperature abnormality; When the temperature difference data does not meet the fourth abnormal judgment requirement, the abnormal judgment result is temperature difference abnormality.

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