CN113820017A - Infrared image-based generator carbon brush temperature monitoring system and temperature prediction method - Google Patents

Infrared image-based generator carbon brush temperature monitoring system and temperature prediction method Download PDF

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CN113820017A
CN113820017A CN202110956798.7A CN202110956798A CN113820017A CN 113820017 A CN113820017 A CN 113820017A CN 202110956798 A CN202110956798 A CN 202110956798A CN 113820017 A CN113820017 A CN 113820017A
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焦尚彬
兀鹏越
李玉军
武晓辉
赵峰
张青
马寅兴
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Xian University of Technology
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Abstract

基于红外图像的发电机碳刷温度监测系统，基于工控机端和移动端平台开发监测软件，利用4G网络，实现多平台远程温度监测；利用云服务器和本地数据管理系统，将数据接入云数据库和本地数据库实现数据的存储与管理；发电机碳刷温度预测方法，包含以下步骤：利用发电机碳刷温度在线监测系统采集现场的红外图像数据，处理后得到碳刷温度数据，采用长短时记忆‑前馈LSTM‑BP神经网络组合模型预测分析碳刷温度数据发展趋势，利用红外图像特征信息与碳刷温度信息融合，通过长短时记忆‑前馈神经网络组合模型提升碳刷温度数据预测精度，实现碳刷温度发展趋势预测。

Figure 202110956798

The generator carbon brush temperature monitoring system based on infrared images, develop monitoring software based on industrial computer terminal and mobile terminal platform, use 4G network to realize multi-platform remote temperature monitoring; use cloud server and local data management system to connect data to cloud database and local database to achieve data storage and management; the generator carbon brush temperature prediction method includes the following steps: using the generator carbon brush temperature online monitoring system to collect on-site infrared image data, processing to obtain carbon brush temperature data, using long and short-term memory ‑The feedforward LSTM‑BP neural network combination model predicts and analyzes the development trend of carbon brush temperature data, uses the fusion of infrared image feature information and carbon brush temperature information, and improves the prediction accuracy of carbon brush temperature data through the long-short-term memory-feedforward neural network combination model. Realize the development trend prediction of carbon brush temperature.

Figure 202110956798

Description

Generator carbon brush temperature monitoring system based on infrared image and temperature prediction method
Technical Field
The invention belongs to the technical field of municipal public facility monitoring, and particularly relates to a generator carbon brush temperature monitoring system and a temperature prediction method based on infrared images.
Background
The electric power is related to the national civilization, and in 2018, the installed capacity of thermal power generating units in China reaches 11 hundred million kilowatts, and the thermal power generating units account for 70 percent of the total capacity. By the end of 2019, the installed capacity of power generation in China is expected to break through 20 hundred million kilowatts for the first time, and the total installed capacity stably occupies the first world. With the increasing of installed capacity, the safety problem of the system is closely concerned, and the reliability of the generator which is one of three main machines of the thermal power generating unit has a great influence on the operation of the thermal power generating unit. The excitation system is the key of safe and stable operation of the generator set.
The carbon brush and the slip ring are used as important components of a synchronous generator excitation system and are weak links of the generator operation. The fault statistics of the generator show that: the faults of the carbon brushes and the slip rings belong to multiple faults, when the carbon brushes jump, the contact resistance of the carbon brushes is increased, the unevenness of current distribution among the carbon brushes is increased, when the current in the carbon brushes exceeds 80A, a series of problems of unstable excitation voltage and current, system power fluctuation and the like can be generated, the heat effect of the current can further burn the carbon brushes, the temperature of the carbon brushes and the slip rings of the generator is abnormally increased, and in severe cases, the ring fire can even be caused, so that the slip rings of the generator are burnt out. Therefore, the quality of the carbon brush directly influences the production safety of the generator, a set of carbon brush temperature monitoring system is researched, and the carbon brush temperature monitoring system has great significance for improving the economic benefit of a power plant.
At present, two methods, namely a point temperature gun temperature measurement method and an infrared temperature sensor temperature measurement method, are mainly used for measuring the temperature of the carbon brush at home and abroad, the point temperature gun temperature measurement method is complex in steps, low in efficiency and prone to errors, and meanwhile, the site environment is dangerous. The infrared temperature sensor temperature measurement method is non-contact measurement, saves complicated wiring and tedious manual inspection, and has the defects that single-point or matrix sensors are mostly adopted for temperature measurement, the coverage area is limited, pixels are low, the quantity of acquired data is small, and meanwhile, the precision is poor, and the requirement of a large-scale power plant cannot be met. Therefore, a set of temperature monitoring system can be researched by the thermal infrared imager, so that the infrared imaging can be realized, the pixel is high, the coverage area is large, and the temperature data can be collected and processed.
A large amount of experimental data show that the carbon brushes of the power plant have different temperature variation trends in different seasons. Meanwhile, the temperature change is not jump but slowly increases. The power plant generally adopts the drawing carbon brush temperature curve, judges the change of carbon brush temperature through the trend, and through the historical data of certain cycle, the analysis predicts the general future trend of whole incident. However, the method of prediction by rule has large error, and only the approximate trend of temperature can be obtained, and the quantitative analysis cannot be well performed. Through the trend prediction of the temperature data of the carbon brush of the generator, the unplanned shutdown accidents of the generator set caused by the faults of collector rings and the carbon brush of the generator can be effectively reduced, and meanwhile, through analysis, the method can provide guiding significance for operation and maintenance personnel on the running state of the carbon brush of the power plant and the load analysis of an excitation system.
Disclosure of Invention
In order to overcome the defects of the prior art, the invention aims to provide a generator carbon brush temperature monitoring system and a temperature data trend prediction method based on infrared images, which can realize remote real-time monitoring of multiple devices, data storage of a database and analysis and prediction of temperature data, and can provide guiding significance for operation and maintenance personnel on analysis of the running state of the carbon brush of the power plant and the load of an excitation system on the basis of improving the detection efficiency.
In order to achieve the purpose, the invention adopts the technical scheme that: the generator carbon brush temperature on-line monitoring system based on the infrared image comprises a carbon brush group arranged on a generator collecting ring; an annular fixing support is arranged on the generator platform, the two thermal infrared imagers are fixed on two sides of the generator through the annular fixing support, and the visual angles of the thermal infrared imagers cover all carbon brushes of the generator; the thermal infrared imager is connected with the LAN port of the industrial 4G router through a twisted pair; the industrial 4G router uploads the on-site data to the cloud server through the 4G base station; the remote control end is provided with an industrial personal computer, generator carbon brush temperature online monitoring software is installed in the industrial personal computer, field data are processed, and the data are stored in a local database; the mobile terminal monitors the field condition by accessing the local database.
The thermal infrared imager adopts an infrared detector of an uncooled focal plane, the pixel value specification is 160 x 120, each infrared image contains 19200 pixels, and the temperature measurement range is-20-150 ℃; the thermal infrared imager is used for acquiring an on-site infrared image and transmitting the image to the cloud server through a network; the thermal infrared imager comprises a first thermal infrared imager and a second thermal infrared imager.
The cloud server accesses the infrared image data of the thermal infrared imager to a network through an industrial router loaded with a 4G internet of things card, and is wirelessly connected with the cloud server through a fixed IP address and the serial number of the thermal infrared imager; the server adopts a 4G industrial router, and the base station adopts a 4G base station.
The on-line monitoring software for the temperature of the carbon brush of the generator comprises upper computer software and database management software, infrared images and carbon brush temperature data measured in an industrial field are displayed in an industrial personal computer in a remote and real-time mode, the on-line monitoring software for the temperature of the carbon brush of the generator has the functions of overtemperature alarm, data recording, image recording, temperature curve drawing, temperature parameter correction and fault diagnosis, and a mobile terminal detection system of an operator is in wireless connection with a data management system through a fixed I P address.
The method for predicting the carbon brush temperature of the generator based on the infrared image comprises the following steps:
step 1, in an on-line monitoring system for the temperature of a carbon brush of a generator based on an infrared image, a field monitoring end acquires infrared image data of a field carbon brush group through an infrared thermal imager and uploads the infrared image data to a cloud server through an industrial router and a 4G base station, a remote industrial controller end obtains temperature data through processing of upper computer software and stores the temperature data to a local database, and then the infrared image data and the carbon brush temperature data in the local database for a period of time are randomly selected;
step 2, performing edge detection on the infrared image data in the step 1 by using a sobel operator, extracting a carbon brush outline in the infrared image data, and filling a missing value by using a corresponding relation between the temperature data of the carbon brush of the generator and the infrared image area of the heating carbon brush;
step 3, using the data filled with the missing values in the step 2, and aiming at the time sequence characteristics of the generator carbon brush temperature data and the characteristics of nonlinearity and influence of multiple factors of the generator carbon brush temperature data, giving play to the time sequence prediction advantages of the long-time memory network, and combining the nonlinear characteristics of the feedforward neural network to construct and train a long-time memory-feedforward neural network combination model;
and 4, predicting the trend of the carbon brush temperature data of the generator by using the long-time memory-feedforward neural network combined model, analyzing the running condition of the carbon brush and the load state of an excitation system according to the trend of the carbon brush temperature data of the generator, drawing an actual temperature curve chart by using the temperature data in the step 1, comparing the actual temperature curve chart with a prediction result, and verifying the prediction precision from the absolute average error, the average absolute percentage error and the root mean square error.
The infrared image data and the temperature data in the step 1 are collected by an on-line monitoring system for the temperature of the carbon brush of the generator and stored in a local database, the local database is accessed at a remote industrial control machine end, and 539 generator carbon brush infrared image data and temperature data which are collected by a power plant continuously for 10 days are selected randomly, wherein the number of training sets is 485, the training sets account for 90% of the total number of the infrared image data and the temperature data of the carbon brush of the generator, the number of test sets accounts for 54%, and the training sets account for 10% of the total number of the infrared image data and the temperature data of the carbon brush of the generator.
Step 2, extracting the carbon brush outline in the infrared image, which comprises the following specific steps:
1) carrying out image binarization; 2) searching a four-connected area by using the bwleabel, wherein the four-connected area refers to a combination which can move in four directions of up, down, left and right from one point on the area, and reaches any pixel in the area on the premise of not exceeding the area; 3) marking the areas judged to be four connected; 4) selecting regions, and only reserving interested regions; 5) calculating the area of the reserved area by using regionprops;
and acquiring a corresponding infrared image in the online monitoring software of the temperature of the carbon brush of the generator, and filling the missing value through the corresponding relation between the temperature data and the area of the reserved area so as to ensure the integrity of the data.
The long-short-term memory-feedforward neural network combination model in the step 3 determines the weight of the long-short-term memory neural network model and the feedforward neural network model by a linear programming method in a series combination mode, and the parameters are set as follows:
long and short time memory neural network model: the input layer and the output layer are all 1 neuron, the hidden layer structure unit is set to be 3 layers, the number of the neurons is 60, 200 and 60, the maximum iteration number is 400, and when the error is less than 10-5When the time comes, the circulation is jumped out;
feedforward neural network model: the input layer and the output layer are all 1 neuron, the feedforward neural network is a double hidden layer and respectively comprises 8 and 4 neurons, the learning coefficient is 0.01, and the error control rate is 10-5Maximum training frequency is 5000 times, training function is train lm, and relative error distribution interval is tested [0.01,0.08]。
In the step 4, a long-time memory-feedforward neural network combined model is used for predicting the trend of the carbon brush temperature data of the generator, the carbon brush temperature data selected in the step 1 is used for drawing an actual temperature curve chart, the result is compared with the actual temperature curve, the evaluation index is used for verifying the prediction result, and the method is implemented according to the following steps:
the evaluation indexes used comprise absolute average error, average absolute percentage error and root mean square error, and the calculation formula is as follows:
Figure BDA0003220618600000061
Figure BDA0003220618600000062
Figure BDA0003220618600000063
in the formula yiThe actual carbon brush temperature value/DEG C of the generator;
Figure BDA0003220618600000064
is the predicted generator carbon brush temperature value/° c; n is the number of test sample sets; 1, 2, …, N;
the experimental test is carried out, the result shows that the method can well predict the data trend of the carbon brush temperature of the generator, the prediction error is mostly stabilized within 0.3 ℃, the running condition of the carbon brush and the load state of the excitation system are analyzed through the temperature data trend by predicting the carbon brush temperature of the generator, and meanwhile, the combined model can provide reasonable reference for the data prediction and decision planning of relevant types.
The invention has the beneficial effects that:
aiming at the problem that the normal operation of equipment is influenced by abnormal temperature rise of a carbon brush of a generator and a slip ring connected with the carbon brush of the generator in the flow guiding process due to the reasons of overhigh current, abnormal cooling device and the like in the carbon brush of a large generator, the invention designs the on-line monitoring system and the method which can remotely monitor operation data in real time and analyze and predict the temperature of the carbon brush;
the method can be used for well predicting the data trend of the carbon brush temperature of the generator, the prediction error is mostly stabilized within 0.3 ℃, and the running condition of the carbon brush and the load state of an excitation system are analyzed through the temperature trend by predicting the carbon brush temperature of the generator. Meanwhile, the combined model can provide reasonable reference for data prediction and decision planning of relevant types.
The device has the advantages that the real-time monitoring of the carbon brush temperature data is accurate due to the adoption of the high-precision thermal infrared imager; the data is accessed to the cloud database and the local database by adopting the 4G network to cooperate with the cloud server to realize data storage and management, so that the system has the advantages of remote monitoring and real-time synchronization; because multi-platform development multifunctional upper computer software is adopted, the system has the advantages of multi-equipment remote temperature monitoring, and also has the functions of overtemperature alarm, data recording, image recording, temperature curve drawing, temperature parameter correction, fault diagnosis and the like; the method of the invention adopts the steps of predicting the data trend of the carbon brush temperature of the generator by the long-time memory-feedforward neural network combined model and verifying the prediction result by using the evaluation index, so the method has the advantages of high prediction precision, nonlinearity and good time sequence characteristic.
Drawings
FIG. 1 is a schematic diagram of the system of the present invention.
Fig. 2 is a software flow chart of the carbon brush temperature analysis system according to the present invention.
Fig. 3 is a diagram of the field collected image and the result (experiment) extracted based on the sobel profile of the generator of the present invention.
FIG. 4 is a system diagram of a long-short-term memory-feedforward neural network combination model according to the present invention, wherein the combination model adopts a flow chart of a series combination mode.
Fig. 5 is a comparison graph (simulation) of the long-time and short-time memory-feedforward neural network prediction result, the long-time and short-time memory neural network prediction result and actually acquired temperature data.
Fig. 6 is a graph of predicted network error (simulation) according to the present invention.
FIG. 7 is a graph of the predicted relative error (simulation) of the present invention.
Detailed Description
In order that the objects, technical solutions and advantages of the present invention will become more apparent, the present invention will be further described in detail with reference to the accompanying drawings in conjunction with the following specific embodiments.
As figure 1 generator carbon brush temperature on-line monitoring system based on infrared image includes: 2 thermal infrared imagers, 4G industrial routers, GPRS/4G base stations, cloud servers, carbon brush temperature analysis systems, data management systems and mobile terminal monitoring systems;
the two thermal infrared imagers are respectively arranged on two sides of the generator, so that the detection range of the thermal infrared imagers is ensured to include the main part of the slip ring of the generator and the whole range of the carbon brush; infrared image data of the thermal imager is transmitted to an industrial 4G router through a twisted pair, a real-time measured data image is transmitted to a cloud server through a 4G/GPRS base station, fig. 2 is a flow chart of on-line monitoring software for the temperature of a carbon brush of a generator, an upper computer carbon brush temperature analysis system developed at a remote industrial computer end acquires the infrared image data on the cloud server for infrared thermal imaging and data analysis, and the upper computer is matched with a MYSQL database to realize the functions of over-temperature alarm, data recording, image recording, temperature curve drawing, temperature parameter correction, fault diagnosis and the like;
as shown in fig. 2, when the carbon brush temperature analysis system is used, each function is initialized according to the default parameter, and each file information configured currently is read. Meanwhile, the software can automatically store the relevant parameters of the generator setting to a software directory for reading and using when the software is opened next time. If the configuration information of each file is normal, clicking ' connect ' to automatically connect the program with the equipment, if not, prompting ' failure to open the configuration file, please reinitialize the parameters! And at the moment, initializing monitoring parameters by software to be default settings, entering a main dialog box interface, clicking a 'play' button on the dialog box interface, creating a display thread, enabling a picture transmitted by the thermal infrared imager and captured specified carbon brush temperature information to be visible in the display thread, and displaying data in real time on a right list.
The method comprises the steps of obtaining carbon brush temperature, selecting whether to draw a real-time curve of a certain point or not, simultaneously clicking to start unit data storage, starting a temperature calculation thread and a data storage thread, calculating temperature data, storing the temperature data into a local database through the data storage thread, automatically judging whether the temperature exceeds the limit or not by a system, recording temperature and time data into the database if the temperature and the time data exceed the limit, starting an overtemperature index display, calling an overtemperature screenshot program to capture and store infrared images, and obtaining the temperature of the generator carbon brush again. And when the carbon brush of the generator fails to operate, an alarm signal is immediately sent to the background system. If the temperature is not over-limit, whether to draw a historical curve or not can be selected, and if a button of drawing the historical curve is clicked, a historical temperature curve graph in the period can be seen after the starting time and the ending time are set. The infrared thermal image can be turned off by clicking a 'stop' button, and the acquisition of the carbon brush temperature is suspended, otherwise the functions of temperature acquisition and monitoring are executed in a circulating mode all the time. Clicking a 'closing' button to release all threads, closing a dialog box to finish software operation, and storing the carbon brush temperature data and the picture obtained in the monitoring process in a software catalogue in a Word report form.
The mobile terminal monitoring system is based on mobile equipment monitoring software such as a mobile phone, and the terminal software realizes wireless connection with the background data management system by accessing the fixed IP address of the management system.
Based on the principle, the invention provides a generator carbon brush temperature data trend prediction method based on infrared images, which comprises the following steps:
step 1, in an on-line monitoring system of the carbon brush temperature of a generator based on infrared images, an on-site monitoring end acquires infrared image data of an on-site carbon brush group 1 through a thermal infrared imager 4 and uploads the infrared image data to a cloud server 8 through an industrial router 6 and a 4G base station 7, a remote industrial controller end 9 obtains temperature data through upper computer software processing and stores the temperature data to a local database 10, and then the infrared image data and the carbon brush temperature data in the local database for a period of time are randomly selected;
the infrared image data and the temperature data are collected and stored in a local database by an on-line monitoring system for the temperature of the carbon brush of the generator, 539 generator carbon brush infrared image data and temperature data which are continuously collected for 10 days by a power plant are randomly selected by accessing the local database at a remote industrial control machine end, wherein the number of training sets is 485 and accounts for 90% of the total number of the infrared image data and the temperature data of the carbon brush of the generator, the number of test sets is 54 and accounts for 10% of the total number of the infrared image data and the temperature data of the carbon brush of the generator;
step 2, performing edge detection on the infrared image data in the step 1 by using a sobel operator, extracting a carbon brush outline in the infrared image data, and filling a missing value by using a corresponding relation between the temperature data of the carbon brush of the generator and the infrared image area of the heating carbon brush;
the specific operation of contour extraction is: 1) carrying out image binarization; 2) searching a four-connected area by using the bwleabel, wherein the four-connected area refers to a combination which can move in four directions of up, down, left and right from one point on the area, and reaches any pixel in the area on the premise of not exceeding the area; 3) marking the areas judged to be four connected; 4) selecting regions, and only reserving interested regions; 5) calculating the area of the reserved area by using regionprops; FIG. 3 is a field collected image of the generator and a result extracted based on a sobel profile;
and acquiring a corresponding infrared image in the online monitoring software of the temperature of the carbon brush of the generator, and filling the missing value through the corresponding relation between the temperature data and the area of the reserved area so as to ensure the integrity of the data.
Step 3, using the data filled with the missing values in the step 2, and aiming at the time sequence characteristics of the generator carbon brush temperature data and the characteristics of nonlinearity and influence of multiple factors of the generator carbon brush temperature data, giving play to the time sequence prediction advantages of the long-time memory network, and combining the nonlinear characteristics of the feedforward neural network to construct and train a long-time memory-feedforward neural network combination model;
fig. 4 is a system block diagram of a long-and-short-term memory-feedforward neural network combination model, the combination model adopts a series combination mode, the long-and-short-term memory neural network model and the feedforward neural network model determine weights by a linear programming method, and the parameters are set as follows:
the long-time memory neural network model comprises an input layer and an output layer which are all 1 neuron, and a hidden layer structure unit is set to be 3 layers and respectively comprises 60, 200 and 60 neurons, the maximum iteration number is 400, and when the error is less than 10-5When the time comes, the circulation is jumped out;
the feedforward neural network model comprises an input layer and an output layer which are all 1 neuron, the feedforward neural network is a double hidden layer and respectively comprises 8 and 4 neurons, the learning coefficient is 0.01, and the error control rate is 1 multiplied by 10-5Maximum training frequency is 5000 times, training function is train lm, and relative error distribution interval is tested [0.01,0.08]；
And 4, predicting the trend of the carbon brush temperature data of the generator by using the long-time memory-feedforward neural network combined model, analyzing the running condition of the carbon brush and the load state of an excitation system according to the trend of the carbon brush temperature data of the generator, drawing an actual temperature curve chart by using the temperature data in the step 1, comparing the actual temperature curve chart with a prediction result, and verifying the prediction precision from the absolute average error, the average absolute percentage error and the root mean square error.
Further, in the step 4, a long-time memory-feedforward neural network combination model is used for predicting the temperature trend of the carbon brush of the generator, the carbon brush temperature data selected in the step 1 is used for drawing an actual temperature curve graph, the result is compared with the actual temperature curve, the predicted result is verified by using an evaluation index, and the used evaluation index is as follows:
Figure BDA0003220618600000121
Figure BDA0003220618600000122
Figure BDA0003220618600000123
in the formula yiThe actual carbon brush temperature value/DEG C of the generator;
Figure BDA0003220618600000124
is the predicted generator carbon brush temperature value/° c; n is the number of test sample sets; i is 1, 2, …, N,
in order to verify the pair prediction effect of the combined model, the data in the step 1 is filled with the missing value by using the method in the step 2, and then the long-time and short-time memory-feedforward neural network combined model established in the step 3 is used for predicting the trend of the temperature, referring to fig. 4, the combined prediction in the figure adopts a series combination mode, and the long-time and short-time memory model and the feedforward model determine the weight by using a linear programming method, and the method mainly comprises the following steps:
firstly, a long-time memory neural network model is used for prediction: 1) loading carbon brush temperature data, and dividing the data into a training set and a test set; 2) normalizing the data, preparing variables for training using the normalized processed data; 3) training a long-time memory neural network by using a train network according to specified training Options, calculating an output value of a long-time memory neural network storage unit and an error item of each long-time memory neural network storage unit according to parameters set in Options, calculating the gradient of each weight by combining the corresponding error items, and updating the weight by adopting a gradient optimization algorithm; 4) when the training is long, the neural network is remembered to be trained well, and the output training effect index jumps out of the training process; 5) predicting by using the trained network;
then, training and predicting the result of the long-time memory neural network prediction by using a feedforward neural network: 6) assigning random numbers in an interval (-1,1) to each connection weight, setting an error function e, giving a calculation precision value beta and a maximum learning frequency M, and randomly selecting a kth input sample and a corresponding expected output; 7) calculating the input and the output of each neuron of the hidden layer, calculating the partial derivative of an error function to each neuron of the output layer, correcting a connection weight omega (k) and a connection weight, and using an optimized feedforward neural network model; 8) calculating a global error, judging whether the network error meets the requirement or not, and finishing training after the error meets a target error when the error meets the preset precision;
calculating a prediction error after model training is completed, and evaluating a prediction result: 9) predicting by using a trained feedforward neural network model, and performing inverse normalization on the obtained result data to obtain a predicted value; 10) and calculating the prediction condition of the model through the evaluation index, and analyzing the prediction precision of the long-time memory-feedforward neural network combined model in comparison with other models.
Fig. 5 is a comparison graph of the long-and-short-term memory-feedforward neural network combined model prediction result, the long-and-short-term memory neural network prediction result and actually acquired temperature data, the training data is arranged at the front section of the curve, the prediction data is arranged at the rear section of the curve, the trend of the carbon brush temperature data of the generator can be well predicted by the long-and-short-term memory-feedforward neural network combined model, and the prediction result is closer to the actual data than the long-and-short-term memory neural network.
FIG. 6 is a predicted network error, and FIG. 6 illustrates that most of the temperature prediction errors are stabilized within 0.3 ℃, and the overall error distribution of the long-time memory-feedforward neural network combined model is superior to the error of the long-time memory neural network.
Fig. 7 shows the predicted relative error, and fig. 7 illustrates that the relative error distribution interval of the long and short term memory neural network is [ -0.008,0.008], and the relative error distribution interval of the long and short term memory-feedforward neural network combined model is [ -0.006,0.008 ].
Table 1 shows performance evaluation of the long-short time memory neural network and the long-short time memory-feedforward neural network combined model for predicting the temperature of the carbon brush of the generator.
TABLE 1
Evaluation index LSTM network BP-LSTM network
MAE 0.1916 0.1601
MAPE 0.0039 0.0033
RMSE 0.2556 0.2380
Table 1 shows that the three evaluation index values of the long-time and short-time memory-feedforward neural network combined model are better than those of the single model, wherein the average absolute error MAE of the combined model is 0.1610, the root mean square error RMSE is 0.238, and both are smaller than those of the single model, which indicates that the combined model has higher prediction accuracy, the performance of the combined model in practical application is better than that of the single model, the combined prediction can more fully exert the advantages of the single model, and the rationality and effectiveness of applying the long-time and short-time memory-feedforward neural network combined model to the carbon brush temperature prediction of the generator are shown.
Experimental tests show that the method can well predict the temperature trend of the carbon brush of the generator, most of prediction errors are stabilized within 0.3 ℃, and the running condition of the carbon brush and the load state of an excitation system are analyzed through the temperature trend by predicting the temperature of the carbon brush of the generator.
Meanwhile, the long-time memory-feedforward neural network combined model can provide reasonable reference for relevant types of data prediction and decision planning.
The foregoing is a preferred embodiment of the present invention, and it will be apparent to those skilled in the art that variations, modifications, substitutions and alterations can be made in the embodiment without departing from the principles and spirit of the invention.

Claims (9)

1.基于红外图像的发电机碳刷温度监测系统，其特征在于，包括有设在发电机集电环(3)上的碳刷组(1)；在发电机平台(2)上设有环形固定支架(5)，两台红外热像仪(4)通过环形固定支架固定于发电机两侧，红外热像仪(4)的视角覆盖发电机的所有碳刷；红外热像仪(4)通过双绞线与工业4G路由器(6)的LAN口相连；工业4G路由器(6)通过4G基站(7)将现场的数据上传至云服务器(8)；远程控制端设有工控机(9)，工控机(9)中安装有发电机碳刷温度在线监测软件，处理现场的数据并将数据存储在本地数据库(10)中；移动端(11)通过访问本地数据库(10)来监测现场情况。1. The generator carbon brush temperature monitoring system based on infrared image is characterized in that, comprises the carbon brush group (1) that is arranged on the generator collector ring (3); A fixed bracket (5), two infrared thermal imagers (4) are fixed on both sides of the generator through annular fixed brackets, and the viewing angle of the infrared thermal imager (4) covers all the carbon brushes of the generator; the infrared thermal imager (4) It is connected to the LAN port of the industrial 4G router (6) through twisted pairs; the industrial 4G router (6) uploads the on-site data to the cloud server (8) through the 4G base station (7); the remote control terminal is provided with an industrial computer (9) , the generator carbon brush temperature online monitoring software is installed in the industrial computer (9) to process the data on site and store the data in the local database (10); the mobile terminal (11) monitors the on-site situation by accessing the local database (10). . 2.根据权利要求1所述的基于红外图像的发电机碳刷温度监测系统，其特征在于，所述的红外热像仪采用非制冷焦平面的红外探测器，像素值规格为160*120，每幅红外图像含有19200个像素点，测温范围为-20℃～150℃；红外热像仪用于采集现场的红外图像，并通过网络将图像传输到云服务器；红外热像仪包括红外热像仪一和红外热像仪二。2. The generator carbon brush temperature monitoring system based on an infrared image according to claim 1, wherein the infrared thermal imager adopts an uncooled focal plane infrared detector, and the pixel value specification is 160*120, Each infrared image contains 19,200 pixels, and the temperature measurement range is -20°C to 150°C; the infrared thermal imager is used to collect on-site infrared images and transmit the images to the cloud server through the network; the infrared thermal imager includes an infrared thermal imager. Imager 1 and Infrared Imager 2. 3.根据权利要求1所述的基于红外图像的发电机碳刷温度监测系统，其特征在于，所述的云服务器将红外热像仪的红外图像数据通过装载4G物联卡的工业路由器接入网络，通过固定的IP地址和红外热像仪的编号与云服务器无线连接。3. The infrared image-based generator carbon brush temperature monitoring system according to claim 1, wherein the cloud server accesses the infrared image data of the infrared thermal imager through an industrial router loaded with a 4G IoT card The network is wirelessly connected to the cloud server through a fixed IP address and the number of the infrared thermal imager. 4.根据权利要求1所述的基于红外图像的发电机碳刷温度监测系统，其特征在于，所述的发电机碳刷温度在线监测软件包括上位机软件和数据库管理软件，将工业现场测得的红外图像和碳刷温度数据远程实时显示在工控机中，发电机碳刷温度在线监测软件具有超温报警，数据记录，图像记录，温度曲线绘制，温度参数修正，故障诊断的功能，运行人员的移动端检测系统以固定的IP地址与数据管理系统进行无线连接。4. The generator carbon brush temperature monitoring system based on infrared image according to claim 1, is characterized in that, described generator carbon brush temperature online monitoring software comprises host computer software and database management software, and the industrial site is measured. The infrared image and carbon brush temperature data are remotely displayed in the industrial computer in real time, and the generator carbon brush temperature online monitoring software has the functions of over-temperature alarm, data recording, image recording, temperature curve drawing, temperature parameter correction, fault diagnosis, and operation personnel. The mobile terminal detection system is wirelessly connected to the data management system with a fixed IP address. 5.基于红外图像的发电机碳刷温度预测方法，其特征在于，包含以下步骤：5. A generator carbon brush temperature prediction method based on an infrared image, characterized in that, comprising the following steps: 步骤1，基于红外图像的发电机碳刷温度监测系统，现场监测端通过红外热像仪(4)采集现场碳刷组(1)的红外图像数据并通过工业路由器(6)和4G基站(7)上传至云服务器(8)，远程工控机端(9)通过上位机软件处理得到温度数据并存储至本地数据库(10)，再随机选取一段时间的本地数据库中的红外图像数据和碳刷温度数据；Step 1, a generator carbon brush temperature monitoring system based on an infrared image, the on-site monitoring end collects the infrared image data of the on-site carbon brush group (1) through an infrared thermal imager (4) and passes the industrial router (6) and the 4G base station (7) ) is uploaded to the cloud server (8), the remote industrial computer terminal (9) obtains the temperature data through the host computer software processing and stores it in the local database (10), and then randomly selects the infrared image data and carbon brush temperature in the local database for a period of time data; 步骤2，对步骤1中的红外图像数据，用sobel算子进行边缘检测并提取红外图像数据中碳刷轮廓，使用发电机碳刷温度数据与发热碳刷的红外图像面积的对应关系，进行缺失值填充；Step 2, for the infrared image data in step 1, use the sobel operator to perform edge detection and extract the outline of the carbon brush in the infrared image data, and use the corresponding relationship between the temperature data of the generator carbon brush and the infrared image area of the heating carbon brush to perform deletion. value fill; 步骤3，使用步骤2中缺失值填充后的数据，针对发电机碳刷温度数据的时序特征，以及发电机碳刷温度数据具有的非线性、受多因素影响的特点，发挥长短时记忆网络的时序预测优点，结合前馈神经网络非线性特征，构建并训练长短时记忆-前馈神经网络组合模型；Step 3, using the data filled with missing values in step 2, aiming at the time series characteristics of the carbon brush temperature data of the generator, as well as the nonlinearity of the temperature data of the generator carbon brush and the characteristics of being affected by multiple factors, the long-term memory network is used. The advantages of time series prediction, combined with the nonlinear characteristics of the feedforward neural network, build and train the long-short-term memory-feedforward neural network combined model; 步骤4，使用长短时记忆-前馈神经网络组合模型进行发电机碳刷温度数据的趋势预测，通过发电机碳刷温度数据趋势来分析碳刷运行状况以及励磁系统负荷状态，使用步骤1中温度数据绘制实际温度曲线图和预测结果比较，从绝对平均误差、平均绝对百分比误差和均方根误差验证预测的精度。Step 4, use the long-term memory-feedforward neural network combination model to predict the trend of the carbon brush temperature data of the generator, and analyze the operation status of the carbon brush and the load state of the excitation system through the trend of the temperature data of the carbon brush of the generator, and use the temperature in step 1. The data is plotted against the actual temperature curve and compared with the predicted results, and the accuracy of the prediction is verified from the absolute mean error, mean absolute percentage error and root mean square error. 6.根据权利要求5所述的基于红外图像的发电机碳刷温度预测方法，其特征在于，步骤1所述的红外图像数据和温度数据为发电机碳刷温度在线监测系统采集并存储到本地数据库中，在远程工控机端访问本地数据库并随机选取电厂连续采集10天的发电机碳刷红外图像数据和温度数据，共539个，其中训练集为485个，占发电机碳刷红外图像数据和温度数据总数的90％，测试集为54个，占发电机碳刷红外图像数据和温度数据总数的10％。6. The infrared image-based generator carbon brush temperature prediction method according to claim 5, wherein the infrared image data and temperature data described in step 1 are collected by the generator carbon brush temperature online monitoring system and stored locally. In the database, access the local database on the remote industrial computer terminal and randomly select the generator carbon brush infrared image data and temperature data collected continuously for 10 days, a total of 539, of which the training set is 485, accounting for the generator carbon brush infrared image data. And 90% of the total temperature data, the test set is 54, accounting for 10% of the total number of generator carbon brush infrared image data and temperature data. 7.根据权利要求5所述的基于红外图像的发电机碳刷温度预测方法，其特征在于，步骤2所述的提取红外图像中碳刷轮廓，具体做法是：7. The method for predicting the temperature of generator carbon brushes based on infrared images according to claim 5, wherein the extraction of carbon brush profiles in the infrared images described in step 2 is as follows: 1)图像二值化；2)利用bwlabel寻找四连通区域，四连通区域是指从区域上一点出发可通过上、下、左、右四个方向移动的组合，在不越出区域的前提下，到达区域内的任意像素；3)对判定为四连通的区域进行标记；4)进行区域的选择，只保留感兴趣的区域；5)利用regionprops计算被保留区域的面积；1) Image binarization; 2) Use bwlabel to find four-connected regions. Four-connected regions refer to a combination of up, down, left, and right directions that can move from a point on the region without going beyond the region. , reach any pixel in the area; 3) mark the area judged to be four-connected; 4) carry out the selection of the area, only keep the area of interest; 5) utilize regionprops to calculate the area of the reserved area; 在发电机碳刷温度在线监测软件中获取对应的红外图像，通过温度数据与被保留区域的面积的对应关系，进行缺失值填充，以保证数据的完整性。Obtain the corresponding infrared image in the generator carbon brush temperature online monitoring software, and fill in the missing values according to the corresponding relationship between the temperature data and the area of the reserved area to ensure the integrity of the data. 8.根据权利要求5所述的基于红外图像的发电机碳刷温度预测方法，其特征在于，步骤3所述的长短时记忆-前馈神经网络组合模型采用串联组合方式将长短时记忆神经网络模型和前馈神经网络模型通过线性规划的方法确定权重，参数设置如下：8. The infrared image-based generator carbon brush temperature prediction method according to claim 5, wherein the long-short-term memory-feedforward neural network combination model in step 3 adopts a series combination method to combine the long-short-term memory neural network The model and the feedforward neural network model determine the weights by linear programming, and the parameters are set as follows: 长短时记忆神经网络模型：输入层，输出层均为1个神经元，隐层结构单元设定为3层，分别含有神经元个数为60，200，60，最大迭代次数为400，当误差小于10-5时，跳出循环；Long-term memory neural network model: the input layer and the output layer are both 1 neuron, the hidden layer structural unit is set to 3 layers, and the number of neurons is 60, 200, and 60 respectively, and the maximum number of iterations is 400. When the error When less than 10 -5 , jump out of the loop; 前馈神经网络模型：输入层，输出层均为1个神经元，前馈神经网络为双隐藏层，分别包含8个和4个神经元，学习系数为0.01，误差控制率为10-5，最大训练次数为5000次，训练函数为trainlm，测试相对误差分布区间[0.01,0.08]。Feedforward neural network model: the input layer and the output layer are both 1 neuron, the feedforward neural network is a double hidden layer, including 8 and 4 neurons respectively, the learning coefficient is 0.01, and the error control rate is 10 -5 , The maximum number of training is 5000 times, the training function is trainlm, and the test relative error distribution interval is [0.01, 0.08]. 9.根据权利要求5所述的基于红外图像的发电机碳刷温度数据预测方法，其特征在于，步骤4中使用长短时记忆-前馈神经网络组合模型进行发电机碳刷温度数据趋势预测，使用步骤1中选取的碳刷温度数据绘制实际温度曲线图，将结果和实际温度曲线进行对比，再使用评价指标验证预测结果，具体按照以下步骤实施：9. The infrared image-based generator carbon brush temperature data prediction method according to claim 5, wherein in step 4, a long-short-term memory-feedforward neural network combined model is used to predict the generator carbon brush temperature data trend, Use the carbon brush temperature data selected in step 1 to draw the actual temperature curve, compare the result with the actual temperature curve, and then use the evaluation index to verify the predicted result, and implement the following steps: 所使用的评价指标包含绝对平均误差、平均绝对百分比误差和均方根误差，其计算公式为：The evaluation indicators used include absolute mean error, mean absolute percentage error and root mean square error, and the calculation formula is:
Figure FDA0003220618590000041
Figure FDA0003220618590000041
Figure FDA0003220618590000042
Figure FDA0003220618590000042
Figure FDA0003220618590000051
Figure FDA0003220618590000051
 式中yi是真实的发电机碳刷温度值/℃；
Figure FDA0003220618590000052
是预测的发电机碳刷温度值/℃；N是测试样本集数量；i＝1，2，…，N；where y i is the real generator carbon brush temperature value/℃;
Figure FDA0003220618590000052
is the predicted generator carbon brush temperature value/°C; N is the number of test sample sets; i=1, 2, ..., N; 进行实验测试，该方法可以很好地预测发电机碳刷温度数据趋势，预测误差大部分稳定在0.3℃以内，通过对发电机碳刷温度进行预测，通过温度数据趋势来分析碳刷运行状况以及励磁系统负荷状态，同时，该组合模型可以为相关类型的数据预测与决策规划提供合理参考。Through experimental tests, this method can well predict the temperature data trend of generator carbon brushes, and most of the prediction errors are stable within 0.3 °C. At the same time, the combined model can provide a reasonable reference for related types of data prediction and decision planning.
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CN115015757A (en) * 2022-08-09 2022-09-06 天津九信科技有限公司 Risk assessment method and system for motor running state
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