CN113836816A - Generator carbon brush temperature monitoring system based on infrared image and temperature prediction method - Google Patents
Generator carbon brush temperature monitoring system based on infrared image and temperature prediction method Download PDFInfo
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Abstract
The invention discloses a generator carbon brush temperature monitoring system and a temperature prediction method based on infrared images, and aims at solving the problem that the normal operation of equipment is influenced by the abnormal rise of the temperature of a generator carbon brush and a slip ring connected with the generator carbon brush in the flow guide process due to the overhigh current and the abnormal cooling device in the large generator carbon brush. The invention develops a generator carbon brush temperature online monitoring system, and multiple platforms are adapted to cooperate with a 4G network to realize multi-equipment remote temperature monitoring; accessing data to a cloud server by using a cloud data management system to realize centralized data storage, collection and management; the method comprises the steps of utilizing actual carbon brush temperature data of a power plant collected by a carbon brush temperature monitoring system, adopting LSTM-BP prediction to analyze carbon brush temperature development trend, utilizing fusion of infrared image characteristic information and carbon brush temperature information, improving carbon brush temperature prediction precision through a combined model, and predicting carbon brush temperature change through data analysis.
Description
Technical Field
The invention relates to the field of municipal public facilities, in particular 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 and draws the carbon brush temperature curve, through the trend, judges the change of carbon brush temperature, through the historical data of certain cycle, and the analysis predicts the trend of whole incident totality future. 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 prediction of the temperature 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
The invention aims to provide a generator carbon brush temperature monitoring system and a temperature 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 provide guidance significance for operation and maintenance personnel on analysis of the running state of a power plant carbon brush 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 following technical scheme:
a generator carbon brush temperature monitoring system based on infrared images comprises a thermal infrared imager, a 4G network, a cloud server and a carbon brush temperature monitoring system;
the infrared thermal 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 measuring range is-20 ℃ to 150 ℃.
And the 4G network and the cloud server access the data of the thermal infrared imager to the network through the industrial router loaded with the 4G internet of things card, and are in wireless connection with the cloud server through the fixed IP address and the thermal imager number.
The carbon brush temperature monitoring system comprises upper computer software developed based on an MFC (micro-fuel cell) and database management software MYSQL (structured query language), can remotely display temperature infrared images measured in an industrial field on an industrial personal computer in real time, and also has the functions of overtemperature alarm, data recording, image recording, temperature curve drawing, temperature parameter correction, fault diagnosis and the like.
The developed mobile terminal detection system is based on software used by mobile equipment such as a mobile phone of an Android Studio development platform, and wireless connection is carried out between the mobile terminal detection system and a data management system through a fixed IP address by using JAVA programming.
Based on the principle, the invention provides a generator carbon brush temperature prediction method based on an infrared image, which comprises the following steps:
step 1: and based on experimental data of a power plant, randomly selecting the temperature data of the carbon brush of the generator for a period of time.
Step 2: and processing the infrared image in the data, performing edge detection by using a sobel operator, extracting the carbon brush outline in the infrared image, and filling the missing value by using the corresponding relation between the temperature data and the infrared image area of the heating carbon brush.
And step 3: and (3) adopting a combined model, exerting the time sequence prediction advantages of the LSTM, and combining the nonlinear characteristics of the BP neural network to construct the LSTM-BP combined model.
And 4, step 4: and (3) predicting the trend of the temperature of the carbon brush of the generator by using an LSTM-BP combined model, analyzing the running condition of the carbon brush and the load state of an excitation system by using the temperature trend, and verifying the prediction accuracy from MAE, MAPE and RMSE.
Further, the generator carbon brush temperature experimental data in the step 1 are 539 generator carbon brush temperature data acquired by randomly selecting a power plant for 11 continuous days, wherein the number of training sets numtrace is 485 and accounts for 90%, and the number of testing sets numtest is 54 and accounts for 10%.
Further, the contour extraction in step 2 is characterized by performing the following operations:
carrying out image binarization; 2) finding connected regions using bwleabel, 4 connected means (pixels are considered connected if they are located above, below, to the left or to the right of other pixels' neighbors); 3) storing label values for the bwing image in the array L (namely, after the connection is judged, the label values are marked in the L matrix); 4) selecting regions, and only reserving interested regions; 5) the area of the reserved region is calculated using regionprops.
After the contour is extracted, the current image is locally captured in the on-line analysis system of the temperature of the carbon brush of the generator, and the missing value is filled according to the corresponding relation between the temperature data and the infrared image area of the heating carbon brush, so that a good effect can be achieved.
Further, the LSTM-BP combined model in step 3 determines the weight of the LSTM model and the BP model by a linear programming method in a series combination manner, and the parameters are set as follows:
the LSTM network model comprises an input layer and an output layer which are respectively 1 neuron, and hidden layer LSTM structural units are set to be 3 layers and respectively contain 60, 200 and 60 neurons. The maximum number of iterations is 400 and when the error is less than 10-5, the loop is exited.
The BP network model comprises an input layer and an output layer which are single neurons, wherein the BP neural network is determined to be a double hidden layer, 8 neurons and 4 neurons respectively through a parameter trial and error method with continuous adjustment, the learning coefficient is 0.01, the error control rate is 1x 10-5, the maximum training frequency is 5000 times, and the training function train is. Testing a relative error distribution interval [0.01,0.08 ];
further, in the step 4, an LSTM-BP combined model is used for predicting the temperature trend of the carbon brush of the generator, the result is compared with an actually acquired temperature curve and the result predicted by using the LSTM model, then the predicted result is verified by using an evaluation index, and the method is implemented according to the following steps:
the evaluation indices used were:
in the formula
The actual carbon brush temperature value/° C of the generator;
is the predicted carbon brush temperature value/° C of the generator; n is the number of test sample sets.
By using matlab simulation, the method can well predict the temperature trend of the carbon brush 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 temperature of the carbon brush of the generator. Meanwhile, the combined model can provide reasonable reference for data prediction and decision planning of relevant types.
Drawings
FIG. 1 is a system configuration diagram 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 shows the field collected image of the generator and the result (experiment) extracted based on the sobel profile
FIG. 4 is a system diagram of an LSTM-BP combined model of the present invention, wherein the combined model adopts a serial combination mode
FIG. 5 is a graph comparing the LSTM-BP prediction result with the LSTM prediction result and the actually collected temperature data (simulation) according to the present invention
FIG. 6 shows the predicted network error (simulation) of the present invention
Fig. 7 shows the predicted relative error of the present invention (simulation).
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 shown in fig. 1, a generator carbon brush temperature 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;
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, a software flow chart of a carbon brush temperature analysis system is shown in FIG. 2, a host computer carbon brush temperature analysis system developed at a remote industrial computer end acquires temperature and image data on the cloud server to perform infrared thermal imaging and data analysis, and the functions of over-temperature alarm, data recording, image recording, temperature curve drawing, temperature parameter correction, fault diagnosis and the like can be realized by matching with a MYSQL database host computer;
the mobile terminal monitoring system is software used by mobile equipment such as a mobile phone based on an Android Studio development platform, and is in wireless connection with the data management system through a fixed IP address by using JAVA programming.
Based on the principle, the invention provides a generator carbon brush temperature prediction method based on an infrared image, which comprises the following steps:
step 1: and based on experimental data of a power plant, randomly selecting the temperature data of the carbon brush of the generator for a period of time.
The experimental data of the carbon brush temperature of the generator are 539 data of the carbon brush temperature of the generator acquired by randomly selecting a power plant for 11 continuous days, wherein the number of training sets numbrain is 485 and accounts for 90%, and the number of testing sets numtest is 54 and accounts for 10%.
Step 2: and processing the infrared image in the data, performing edge detection by using a sobel operator, extracting the carbon brush outline in the infrared image, and filling the missing value by using the corresponding relation between the temperature data and the infrared image area of the heating carbon brush.
Contour extraction requires the following operations: 1) carrying out image binarization; 2) finding connected regions using bwleabel, 4 connected means (pixels are considered connected if they are located above, below, to the left or to the right of other pixels' neighbors); 3) storing label values for the bwing image in the array L (namely, after the connection is judged, the label values are marked in the L matrix); 4) selecting regions, and only reserving interested regions; 5) the area of the reserved region is calculated using regionprops. FIG. 3 shows the field collected image of the generator and the result extracted based on the sobel profile
After the contour is extracted, the current image is locally captured in the on-line analysis system of the temperature of the carbon brush of the generator, and the missing value is filled according to the corresponding relation between the temperature data and the infrared image area of the heating carbon brush, so that a good effect can be achieved.
And step 3: and (3) adopting a combined model, exerting the time sequence prediction advantages of the LSTM, and combining the nonlinear characteristics of the BP neural network to construct the LSTM-BP combined model.
FIG. 4 is a system diagram of an LSTM-BP combined model, the combined model adopts a serial combination mode, the LSTM model and the BP model determine weights by a linear programming method, and the parameters are set as follows:
the LSTM network model comprises an input layer and an output layer which are respectively 1 neuron, and hidden layer LSTM structural units are set to be 3 layers and respectively contain 60, 200 and 60 neurons. The maximum number of iterations is 400 and when the error is less than 10-5, the loop is exited.
The BP network model comprises an input layer and an output layer which are single neurons, wherein the BP neural network is determined to be a double hidden layer, 8 neurons and 4 neurons respectively through a parameter trial and error method with continuous adjustment, the learning coefficient is 0.01, the error control rate is 1x 10-5, the maximum training frequency is 5000 times, and the training function train is. Testing a relative error distribution interval [0.01,0.08 ];
and 4, step 4: and predicting the trend of the generator carbon brush temperature by using an LSTM-BP combined model, analyzing the running condition of the carbon brush and the load state of an excitation system according to the temperature trend, and verifying the prediction precision from the MAE, the MAPE and the RMSE.
Further, in the step 4, an LSTM-BP combined model is used for predicting the temperature trend of the carbon brush of the generator, the result is compared with the actually acquired temperature curve and the result predicted by using the LSTM model, and then the predicted result is verified by using an evaluation index, wherein the used evaluation index is as follows:
in the formula
The actual carbon brush temperature value/° C of the generator;
is the predicted carbon brush temperature value/° C of the generator; n is the number of test sample sets.
In order to verify the prediction effect of the combined model, the data in the step 1 is filled with missing values by using the method in the step 2, the trend of the LSTM-BP combined model established in the step 3 on the temperature is predicted, and matlab simulation is used:
FIG. 5 is a comparison graph of the LSTM-BP prediction result, the LSTM prediction result and the actually collected temperature data, the front section of the curve is training data, and the rear section is prediction data
FIG. 6 is a graph of predicted network errors
FIG. 7 is a graph of predicted relative error
Table 1 shows the performance evaluation (simulation) of the LSTM network and BP-LSTM network for predicting the temperature of the carbon brush of the generator according to the invention
TABLE 1
Simulation experiments show that the method can well predict the temperature trend of the carbon brush 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 temperature of the carbon brush of the generator.
Meanwhile, the combined model can provide reasonable reference for data prediction and decision planning of relevant types.
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 (6)
1. A generator carbon brush temperature monitoring system based on infrared images is characterized by comprising a thermal infrared imager, a 4G network, a cloud server and a carbon brush temperature monitoring system;
the infrared thermal 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 measuring range is-20 ℃ to 150 ℃;
the 4G network and the cloud server access the data of the thermal infrared imager to the network through the industrial router loaded with the 4G internet of things card, and the data are wirelessly connected with the cloud server through the fixed IP address and the thermal imager number;
the carbon brush temperature monitoring system comprises upper computer software developed based on an MFC (micro-fuel cell) and database management software MYSQL (structured query language), can remotely display temperature infrared images measured in an industrial field on an industrial personal computer in real time, and also has the functions of overtemperature alarm, data recording, image recording, temperature curve drawing, temperature parameter correction and fault diagnosis.
2. A generator carbon brush temperature prediction method based on infrared images is characterized by comprising the following steps:
step 1: randomly selecting temperature data of the carbon brush of the generator for a period of time based on experimental data of a power plant;
step 2: processing an infrared image in the data, performing edge detection by using a sobel operator, extracting a carbon brush outline in the infrared image, and filling a missing value by using a corresponding relation between temperature data and the infrared image area of the heating carbon brush;
and step 3: the method comprises the steps of adopting a combined model, exerting the time sequence prediction advantages of the LSTM, and combining the nonlinear characteristics of a BP neural network to construct an LSTM-BP combined model;
and 4, step 4: and (3) predicting the trend of the temperature of the carbon brush of the generator by using an LSTM-BP combined model, analyzing the running condition of the carbon brush and the load state of an excitation system by using the temperature trend, and verifying the prediction accuracy from MAE, MAPE and RMSE.
3. The method for predicting the temperature of the carbon brush of the generator based on the infrared image as claimed in claim 2, wherein the experimental data of the temperature of the carbon brush of the generator in the step 1 are 539 collected by randomly selecting the carbon brush of the generator for 11 continuous days of a power plant, wherein the training sets numtrace are 485 and account for 90%, and the testing sets numtest are 54 and account for 10%.
4. The method for predicting the carbon brush temperature of the generator based on the infrared image as claimed in claim 2, wherein the contour extraction in the step 2 is characterized by comprising the following steps of:
carrying out image binarization; 2) finding connected regions using bwleabel, 4 connected means (pixels are considered connected if they are located above, below, to the left or to the right of other pixels' neighbors); 3) storing label values for the bwing image in the array L (namely, after the connection is judged, the label values are marked in the L matrix); 4) selecting regions, and only reserving interested regions; 5) calculating the area of the reserved area by using regionprops;
after the contour is extracted, the current image is locally captured in the on-line analysis system of the temperature of the carbon brush of the generator, and the missing value is filled according to the corresponding relation between the temperature data and the infrared image area of the heating carbon brush, so that a good effect can be achieved.
5. The method for predicting the carbon brush temperature of the generator based on the infrared image as claimed in claim 2, wherein the LSTM-BP combined model in the step 3 determines the weight of the LSTM model and the BP model through a linear programming method in a series combination mode, and the parameters are set as follows:
the LSTM network model comprises an input layer and an output layer which are respectively 1 neuron, and hidden layer LSTM structural units are set to be 3 layers and respectively contain 60, 200 and 60 neurons. The maximum number of iterations is 400 and when the error is less than 10-5, the loop is exited.
The BP network model comprises an input layer and an output layer which are single neurons, wherein the BP neural network is determined to be a double hidden layer by continuously adjusting a parameter trial-and-error method, 8 neurons and 4 neurons are respectively arranged, the learning coefficient is 0.01, the error control rate is 1x 10-5, the maximum training frequency is 5000 times, the training function train is, and a relative error distribution interval is tested [0.01,0.08 ].
6. The method for predicting the carbon brush temperature of the generator based on the infrared image as claimed in claim 2, wherein in the step 4, an LSTM-BP combined model is used for predicting the temperature trend of the carbon brush of the generator, the result is compared with an actually acquired temperature curve and the result predicted by using the LSTM model, and then the evaluation index is used for verifying the prediction result, which is implemented according to the following steps:
the evaluation indices used were:
the real carbon brush temperature value/° C of the generator is obtained in the formula; is the predicted carbon brush temperature value/° C of the generator; n is the number of test sample sets,
and (3) carrying out a simulation experiment, wherein the method can well predict the temperature trend of the carbon brush of the generator, most of prediction errors are stabilized within 0.3 ℃, 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, and meanwhile, the combined model can provide reasonable reference for data prediction and decision planning of relevant types.
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| CN115879054A (en) * | 2023-03-03 | 2023-03-31 | 泰安市特种设备检验研究院 | Method and device for determining liquid ammonia refrigeration state based on image processing |
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