CN113435579A - Intelligent power equipment identification method based on deep neural network - Google Patents
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Abstract
The invention discloses an intelligent identification method of power equipment based on a deep neural network, belonging to the technical field of intelligent power grid information and comprising the following steps: s1: constructing a data set; s2: constructing a deep neural network; s3: and performing deep neural network training by taking the data set as a training set, selecting a neural network with optimal performance through multiple loop iterations, establishing a deep neural network model, and intelligently identifying various electric power equipment in the electric power system by using the deep neural network model. According to the invention, intelligent identification of the power equipment in the power system is realized through the trained deep neural network model, the time cost and the labor cost of artificial judgment can be greatly reduced, and intelligent perception is carried out through the proposed scheme and the combination with a perfect monitoring and monitoring system in the power system, so that a foundation is laid for ubiquitous power internet construction.
Description
Technical Field
The invention relates to the technical field of intelligent power grid information, in particular to an intelligent identification method for power equipment based on a deep neural network.
Background
The construction of the ubiquitous power internet conforms to the development trend of the integration of the energy revolution and the digital revolution, and the ubiquitous power internet practices a new development concept and is a new leading engineering in the power industry. The ubiquitous power internet of things is an intelligent service system which is characterized by comprehensive state sensing, efficient information processing and convenient and flexible application, fully applies modern information technologies such as mobile interconnection and artificial intelligence and advanced communication technologies around each link of a power system, realizes the mutual object interconnection and man-machine interaction of each link of the power system, and is applied to the power system. The ubiquitous power internet of things comprises a sensing layer, a network layer, a platform layer and an application layer, wherein the sensing layer mainly solves the problem of data acquisition and is a data entry constructed in the ubiquitous power internet.
In current electric power system, intelligent patrolling and examining such as high definition video monitoring, thermal infrared imager have obtained large-scale application, and all kinds of robots of patrolling and examining, unmanned aerial vehicle etc. carry on camera device, realize omnidirectional control to power equipment. Besides intelligent inspection, a large amount of map information can be acquired by traditional manual inspection methods such as infrared temperature measurement and ultraviolet imaging detection. At present, massive map data still need to be analyzed and identified by operating personnel, and defects are counted and reported, so that effective information cannot be provided from massive maps and videos, and automatic identification and judgment are performed, and the construction and development of the power internet are fundamentally restricted.
The patent document with publication number CN107067026A discloses a method for detecting faults of power equipment based on a deep neural network, which comprises the steps of collecting an infrared spectrum, and constructing a normalized spectrum database; establishing a multilayer deep neural network to classify the infrared spectrum for testing; establishing a target detection framework RCNN for carrying out equipment partition on the classified test infrared spectrum; setting a temperature early warning rule in a subarea according to an infrared spectrum defect analysis criterion; and obtaining a device safety analysis conclusion and realizing early warning according to the temperature early warning rule. After the method is applied to power grid equipment detection, tedious work such as manual equipment partition identification can be greatly reduced, and the work efficiency is improved.
Patent document No. CN109784348A discloses an infrared thermal imaging-based power equipment identification and online diagnosis method and system; the method comprises the steps of firstly collecting infrared image data of the power equipment, making virtual data for learning, and then transferring a deep neural network model to form a power equipment infrared recognition deep neural network model; establishing a defect rule base for classifying infrared power equipment and components according to the infrared diagnosis application standard of the charged equipment in the power industry; and finally, deploying a mobile power equipment recognition deep neural network model at the mobile terminal, carrying out power equipment and part recognition on the infrared image collected in real time, carrying out online analysis and diagnosis on the equipment thermal field distribution according to a defect rule base, and evaluating the current running state of the power equipment.
The detection method can be used for detecting the power equipment to a certain extent, but the overall generalization capability is poor, multiple loop iterations are not performed, and the detection result precision is not high.
Disclosure of Invention
In view of the above, an object of the present invention is to provide an intelligent identification method for power devices based on a deep neural network, in order to overcome the disadvantages of the prior art.
In order to achieve the purpose, the invention adopts the following technical scheme:
the intelligent identification method of the power equipment based on the deep neural network comprises the following steps:
s1: constructing a data set;
s2: constructing a deep neural network;
s3: and performing deep neural network training by taking the data set as a training set, selecting a neural network with optimal performance through multiple loop iterations, establishing a deep neural network model, and intelligently identifying various electric power equipment in the electric power system by using the deep neural network model.
Further, in step S1, the data set is a map of various devices in the power system and is labeled.
Further, in step S3, the deep neural network training method includes:
(1) transmitting the input data into a neural network, and outputting the input data through a mapping network;
(2) comparing the output result with the known calibration, judging whether the output result is consistent with the known calibration, if not, updating the mapping network according to the error, and finally enabling the output to be matched with the known result.
Further, the mapping variables of the mapping network are stored, namely the trained neural network model is obtained.
Furthermore, the trained deep neural network is tested, the accuracy of correctly identifying the power transformation equipment is counted, and when the accuracy meets the requirement, the deep neural network is shown to be good in performance.
The normal operation of the power equipment is the basis that a power system can work safely and stably for a long time, so that the regular inspection of the power equipment is very important. The regular checking of the operating states of various power devices within a power system is an important method for maintaining the normal operation of the power system. The conventional power equipment basically checks the operating state thereof by manually and regularly inspecting. With the rapid development of artificial intelligence, image recognition is used as an important application of a deep neural network technology and is widely applied to various industries in the society. The gradual development and maturity of technologies such as artificial intelligence technology and image processing can well make up the defects of the traditional method when the artificial intelligence technology and the image processing technology are applied to the detection of the running state of the power equipment, and the method has a profound development prospect. For example, the invention patent with the publication number of CN107607207B discloses a method, a system and an electronic device for diagnosing thermal failure of an electric power device. The method comprises the following steps: acquiring an infrared image of the power equipment, and constructing a convolutional neural network model according to the infrared image; inputting an infrared image to be detected into the convolutional neural network model, and identifying a temperature scale and power equipment in the infrared image through the convolutional neural network model; generating an RGB value and temperature reference table according to the RGB value of the identified pixel point of the temperature scale and the upper and lower boundaries of the temperature scale, extracting the RGB value of the identified power equipment, and comparing the extracted RGB value with the RGB value in the RGB value and temperature reference table to obtain the temperature result of the identified power equipment; and diagnosing the temperature result according to the power grid system diagnosis standard, and judging whether the electrical equipment has a thermal fault. According to the invention, the power equipment is efficiently and accurately identified through the convolutional neural network model, the temperature is accurately read through the RGB value, and the intelligent level of a power grid system is improved. Patent document with publication number CN112613454A discloses a method and system for recognizing violation at power infrastructure construction site, which obtains video frame image data of power infrastructure construction site; extracting the equipment characteristics of the video frame image by using a first neural network model to obtain an equipment characteristic diagram; according to the obtained equipment feature map, a preset candidate region is used for generating a network to obtain feature candidate regions with various scales and aspect ratios; obtaining an equipment identification result according to the obtained equipment feature map and the feature candidate area by using a preset classification regression network, and carrying out on-site violation judgment according to the equipment identification result; the adoption of the artificial intelligent deep learning algorithm realizes the rapid identification of various violations on the electric power infrastructure construction site, and improves the intelligent identification efficiency and accuracy of the violations.
However, the traditional machine neural network method is limited by artificial feature extraction, can effectively identify a specific image set, but has poor overall generalization capability and certain limitations. Compared with the traditional image identification method, the image identification is carried out by utilizing the deep neural network algorithm, the rules do not need to be set artificially, more accurate characteristics can be extracted through the deep network structure, and the accuracy of the image identification can be effectively improved. Therefore, by utilizing the deep neural network theory, the real-time intelligent identification of the power equipment is carried out, and the identification result is used as the entrance of the sensing layer of the ubiquitous power internet, so that the dimension reduction storage and transmission of massive power equipment maps can be carried out, the data capacity is reduced, the intelligent level of map identification can be improved, and the intelligent sensing contribution is made to the intelligent sensing aspect of the ubiquitous power internet construction.
The invention has the beneficial effects that:
according to the invention, firstly, various devices in the power system need to be subjected to atlas collection and labeling, then the atlas collection and labeling is used as a training set to carry out deep neural network training, a neural network with the optimal performance is selected through multiple cycle iterations, and the purpose of intelligently identifying various power devices in the power system can be realized by utilizing the deep neural network model. The scheme provided by the invention can not only extract the information of the power equipment from the existing massive maps and videos in the power system, but also be used as a monitoring network to be combined with various intelligent robots, so that the real-time identification of the power equipment is realized, the identification result is used as output, and different detection and diagnosis are carried out according to different equipment types. The scheme provided is to be used as a perception layer entrance of the ubiquitous power internet, the recognition result is used as information, and the information is transmitted and utilized on a network layer, a platform layer and an application layer of the ubiquitous power internet, so that the intelligent level of the construction of the power internet is further improved, and the construction of the power energy internet is tiled.
Through the deep neural network model after training, realize carrying out intelligent recognition to the power equipment in the electric power system, time cost and the human cost that can the artificial judgement of greatly reduced through the scheme that proposes to combine together with perfect control and monitoring system in the electric power system, carry out intelligent perception, lay the foundation for ubiquitous electric power internet construction.
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The present invention will be described in further detail with reference to the accompanying drawings.
FIG. 1 is a flow chart of neural network training and prediction according to the present invention.
FIG. 2 is a diagram of a deep neural network architecture of the present invention.
Detailed Description
The technical solution of the present invention will be described clearly and completely with reference to fig. 1-2 and specific embodiments, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, not all of them. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Example 1
The intelligent identification method of the power equipment based on the deep neural network comprises the following steps:
s1: constructing a data set;
s2: constructing a deep neural network;
s3: and performing deep neural network training by taking the data set as a training set, selecting a neural network with optimal performance through multiple loop iterations, establishing a deep neural network model, and intelligently identifying various electric power equipment in the electric power system by using the deep neural network model.
In step S1, the data set is a map of various devices in the power system and labeled.
In step S3, the deep neural network training method includes:
(1) transmitting the input data into a neural network, and outputting the input data through a mapping network;
(2) comparing the output result with the known calibration, judging whether the output result is consistent with the known calibration, if not, updating the mapping network according to the error, and finally enabling the output to be matched with the known result.
And storing the mapping variable of the mapping network, namely the trained neural network model, inputting the map of the equipment to be tested, namely identifying the equipment, monitoring the state of the equipment and outputting a result.
And testing the trained deep neural network, counting the accuracy of correctly identifying the power transformation equipment, and when the accuracy meets the requirement, indicating that the deep neural network is good in performance.
In fig. 1, it can be seen that, when the neural network is used for prediction, training of the neural network is first required, where the training of the neural network refers to transferring input data into the neural network, outputting the input data through the mapping network, comparing an output result with a known calibration, determining whether the output data is consistent with the known calibration, and if the output data is not consistent with the known calibration, updating the mapping network according to an error, and finally enabling the output to be matched with the known result. And storing the mapping variable at the moment, namely the mapping variable is the trained neural network model, and when prediction is carried out, inputting predicted data into the trained neural network to obtain a prediction result.
Through the deep neural network model after training, realize carrying out intelligent recognition to the power equipment in the electric power system, time cost and the human cost that can the artificial judgement of greatly reduced through the scheme that proposes to combine together with perfect control and monitoring system in the electric power system, carry out intelligent perception, lay the foundation for ubiquitous electric power internet construction.
Example 2
On the basis of embodiment 1, fig. 2 is a diagram illustrating a deep neural network structure according to the present invention, where predicted information is limited for a single-layer neural network, and therefore, a deep neural network model is constructed by increasing the number of neural network layers, so that complex prediction can be achieved. In fig. 2, the deep neural network model is an AlexNet model, and has 8 hidden layers, wherein the first five layers are convolutional layers, which are deep convolutional neural networks, and the last layer is a classification layer, which is used for classifying the atlas. The method described in the present embodiment will be described by taking an AlexNet model as an example.
(1) Calibrating and storing different power equipment maps in a classified manner;
(2) and adjusting the size of the atlas according to the AlexNet deep neural network model, wherein the size of the input atlas is 227 x 3.
(3) Importing the atlas to be trained into an AlexNet deep neural network model for deep training;
(4) and through multiple cycles, selecting the depth neural network with the best performance, and identifying the power equipment by using the depth neural network.
The invention provides an intelligent identification scheme of electric power equipment based on a deep neural network. The proposed scheme is used as a perception layer entrance of the ubiquitous power internet and lays a foundation for the construction of the ubiquitous power internet.
Finally, the above embodiments are only used for illustrating the technical solutions of the present invention and not for limiting, and other modifications or equivalent substitutions made by the technical solutions of the present invention by those of ordinary skill in the art should be covered within the scope of the claims of the present invention as long as they do not depart from the spirit and scope of the technical solutions of the present invention.
Claims (5)
1. The intelligent identification method of the power equipment based on the deep neural network is characterized by comprising the following steps: comprises the following steps:
s1: constructing a data set;
s2: constructing a deep neural network;
s3: and performing deep neural network training by taking the data set as a training set, selecting a neural network with optimal performance through multiple loop iterations, establishing a deep neural network model, and intelligently identifying various electric power equipment in the electric power system by using the deep neural network model.
2. The intelligent deep neural network-based power equipment identification method according to claim 1, wherein the method comprises the following steps: in step S1, the data set is a map of various devices in the power system and labeled.
3. The intelligent deep neural network-based power equipment identification method according to claim 1, wherein the method comprises the following steps: in step S3, the deep neural network training method includes:
(1) transmitting the input data into a neural network, and outputting the input data through a mapping network;
(2) comparing the output result with the known calibration, judging whether the output result is consistent with the known calibration, if not, updating the mapping network according to the error, and finally enabling the output to be matched with the known result.
4. The intelligent deep neural network-based power equipment identification method according to claim 3, wherein the method comprises the following steps: and storing the mapping variables of the mapping network, namely the trained neural network model.
5. The intelligent deep neural network-based power equipment identification method according to claim 1, wherein the method comprises the following steps: and testing the trained deep neural network, counting the accuracy of correctly identifying the power transformation equipment, and when the accuracy meets the requirement, indicating that the deep neural network is good in performance.
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| CN108304931A (en) * | 2018-02-06 | 2018-07-20 | 国网天津市电力公司电力科学研究院 | A kind of Condition-based Maintenance of Substation Equipment method for diagnosing faults |
| CN109212392A (en) * | 2018-09-25 | 2019-01-15 | 上海交通大学 | Recognition methods, system and the Caffe convolutional neural networks of direct current cables shelf depreciation defect failure |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN108304931A (en) * | 2018-02-06 | 2018-07-20 | 国网天津市电力公司电力科学研究院 | A kind of Condition-based Maintenance of Substation Equipment method for diagnosing faults |
| CN109212392A (en) * | 2018-09-25 | 2019-01-15 | 上海交通大学 | Recognition methods, system and the Caffe convolutional neural networks of direct current cables shelf depreciation defect failure |
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Application publication date: 20210924 |