CN112381210A - Coal-fired unit water-cooling wall temperature prediction neural network model - Google Patents
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Abstract
The invention discloses a neural network model for predicting wall temperature of a water-cooled wall of a coal-fired unit, which is characterized in that: the device is composed of a plurality of neural networks which are connected in turn, wherein each neural network is composed of an input layer, a hidden layer and an output layer; the input layer is divided into three types of input of a predicted heating surface wall temperature variable, an upstream heating surface wall temperature variable and other key variable, not only key factors influencing the wall temperature and the change condition of the upstream heating surface wall temperature are considered, but also the influence of the historical data of the predicted heating surface wall temperature on the input layer is considered; determining an input variable structure, correcting an input parameter delay coefficient, the number of hidden layers and an activation function, improving model training and generalization precision, obtaining the variation trend of the predicted wall temperature at different moments through successive wall temperature prediction, and realizing better water-cooled wall temperature prediction precision.
Description
Technical Field
The invention relates to the technical field of thermal power generating unit heating surface wall temperature characteristic modeling, in particular to a coal-fired unit water-cooling wall temperature prediction neural network model.
Background
Under increasingly serious environmental protection pressure, the state continuously pushes the energy structure adjustment, and clean energy such as wind energy, solar energy and the like is continuously and rapidly developed. However, new energy sources such as wind energy, solar energy and the like generally have the characteristics of randomness and intermittence, and large-scale grid connection inevitably has certain influence on the safety and stability of a power grid; on the other hand, with the slow increase of the economic growth speed and the large adjustment of the economic structure, the whole power supply and demand contradiction in China is changed from shortage to relative surplus. Therefore, in order to improve the consumption capacity of new energy such as wind energy, solar energy and the like, the thermal power generating unit must bear a large peak regulation duty, and the long-term low-load operation of the thermal power generating unit must become a normal state.
On the other hand, the domestic unit generally has the defects that the actual coal is deviated from the designed coal type, the coal quality is seriously mixed and burnt, the quality of the coal fed into the boiler is good and bad, the actual coal quality is deviated from the designed coal type, and the design condition of the boiler is fundamentally changed. In the actual operation process of the unit, the air powder at four corners of the boiler has certain deviation, which easily causes the problems of partial burning, coking and the like of the boiler and the problem of pipe explosion of a water-cooled wall.
The safety of hydrodynamic working condition, when the boiler operates under low load condition, the degree of flame full in the boiler is different from high load time difference, so that the heat load of the hearth is uneven. The water circulation stop and circulation backflow can be caused by the fact that the steam-water flow distribution deviation between each circulation loop of the water cooling wall and the adjacent pipes is increased. During low-load operation, the water cooling wall overtemperature phenomenon is easy to occur due to factors in combustion and safety factors of boiler water circulation. Therefore, during low-load operation, real-time measurement monitoring of the wall temperature of the water wall and advanced prediction and control of the wall temperature are effective ways for reducing the risk of pipe explosion.
At present, the thermal power generating unit mainly adopts the following two measurement schemes for wall temperature:
1) wall temperature measurement is realized by installing a large number of thermocouple wall temperature measuring points at the metal parts of the tube walls of the boiler superheater, the reheater, the water wall and the like, and an independent monitoring system or a DCS (distributed control system) system is directly connected for direct monitoring, so that the safety and stability of long-term operation of the boiler are improved; at present, the method has higher requirements on the environment around a measuring point, but the environment in a furnace is often severe, and has certain influence on the measuring precision and accuracy; meanwhile, the method can only measure the temperature value at the current moment, and only when the measuring points are over-temperature due to more measuring points, an alarm can be given out, so that the operating personnel can correspondingly adjust the boiler parameters according to actual experience. Therefore, operators can not judge a large number of wall temperature measuring points in real time in the process of monitoring the wall temperature overtemperature, and can not solve the overtemperature problem in time when overtemperature alarming is carried out, and adverse influence is brought to the operation safety of the boiler.
2) And establishing a wall temperature prediction model by a mechanism or mathematical analysis method, thereby realizing the calculation and prediction of the wall temperature. The method is complex, has more boundary parameters, can not give all boundary parameters for actual measuring points of the power plant, and needs to be continuously corrected under different conditions of the model, so that the method does not meet the requirement of on-line calculation and can not participate in closed-loop control of the wall temperature of the power plant in real time; based on a mathematical modeling analysis method, a wall temperature prediction method based on an artificial neural network is mostly adopted at present, only the influence of external factors on the wall temperature is considered, and static network structures such as a BP neural network and the like are adopted to predict the wall temperature of a boiler tube. The current wall temperature historical data, the upstream wall temperature historical data and the change rate of related factors are not considered, the contents of a time sequence prediction neural network structure, a neural network activation function and the like are not researched, the prediction structure is laggard, and the calculation result is poor.
In summary, the existing wall temperature overtemperature countermeasures and prediction means only stop displaying alarm, so that the parameters are changed by means of the experience of operators, and closed-loop control is not realized. On the other hand, the wall temperature prediction model needs to be optimized, so that accurate prediction of the wall temperature is realized, and advanced closed-loop operation is realized to avoid overtemperature of the wall temperature.
Disclosure of Invention
In order to solve the problems in the prior art, the invention aims to provide a neural network model for predicting the wall temperature of the water-cooled wall of a coal-fired unit, which considers the historical data of the current wall temperature, the historical data of the upstream wall temperature, the change rate of relevant factors and the like, determines an input variable structure, obtains the change trend of the predicted wall temperature at different moments through successive wall temperature prediction and realizes better wall temperature prediction precision.
In order to achieve the purpose, the prediction model adopts the following technical scheme:
a neural network model for predicting wall temperature of a water-cooled wall of a coal-fired unit is formed by connecting a plurality of neural networks in a successive mode, wherein each neural network consists of an input layer, a hidden layer and an output layer; the input layer is divided into three types of input of a predicted heating surface wall temperature variable, an upstream heating surface wall temperature variable and other key variable, not only key factors influencing the wall temperature and the change condition of the upstream heating surface wall temperature are considered, but also the influence of the historical data of the predicted heating surface wall temperature on the input layer is considered;
inputting the predicted heating surface wall temperature variable, namely the maximum value of the wall temperature of a vertical water-cooled wall of a certain wall of the boiler; the variable input of the wall temperature of the upstream heating surface refers to the temperature of the upper-stage heating surface or equipment of the vertical water-cooled wall, namely the average value of the steam temperature at the outlet of the economizer and the wall temperature of the spiral water-cooled wall; the other key variable inputs are parameters which obviously influence the wall temperature of the vertical water wall by the change of signals and comprise the actual load of a unit, the coal quantity, the primary air pressure and the secondary air flow; and the output layer is a predicted value of the maximum wall temperature of a vertical water-cooled wall of a certain wall of the boiler.
The input neurons of the first neural network in the plurality of neural networks comprise seven input neurons of the maximum wall temperature of a vertical water-cooled wall of a certain wall of a boiler, the temperature of steam at an outlet of an economizer, the average value of the wall temperature of a spiral water-cooled wall, the actual load of a unit, the coal quantity, the primary air pressure and the secondary air flow; the first neural network output neuron is a predicted value of the wall temperature maximum value of the vertical water-cooled wall in the prediction period 1; inputting and connecting a first neural network output neuron with a second neural network, sending a predicted value of the maximum value of the wall temperature of the vertical water-cooling wall in the prediction period 1 into a maximum value of the wall temperature of the vertical water-cooling wall in the second neural network and inputting the neuron into the neural network for updating historical data, connecting the rest input neurons with six input neurons of steam temperature at an outlet of an economizer, the average value of the wall temperature of the spiral water-cooling wall, the actual load of a unit, the coal quantity, primary air pressure and secondary air flow, and inputting and updating, wherein the second neural network output neuron is the predicted value of the maximum value of the wall temperature of the vertical water-cooling wall in the prediction period 2; analogizing in sequence to obtain a prediction result of the nth neural network output neuron, namely a prediction value of the wall temperature maximum value of the vertical water-cooling wall in the prediction period n; and the hidden layer neurons in the neural network model are respectively connected with all input neurons and all output neurons, normalized input variables of the input neurons of the neural network are multiplied by the weight values of all inputs, and the neuron output is obtained by calculating and superposing neuron bias through an activation function.
The mathematical relationship between the plurality of neural network output neurons and input neurons is represented by the following equation:
1 st neural network:
Z(1)＝f(X1(t-1),X1(t-2),…X1(t-P),X2(t-1),X2(t-2),…X2(t-P),…Y(t-1),Y(t-2),…Y(t-P))
the 2 nd neural network:
Z(2)＝f(X1(t-1),X1(t-1),X1(t-2),…X1(t-P+1),X2(t-1),X2(t-1),X2(t-2),…X2(t-P+1),…Z(1),Y(t-1),Y(t-2),…Y(t-P+1))
···
the nth neural network:
wherein, X1、X2…X6Respectively representing 6 influencing factors in an input layer of the neural network, namely six inputs of steam temperature at an outlet of an economizer, an average value of wall temperature of a spiral water-cooled wall, actual load of a unit, coal quantity, primary air pressure and secondary air flow; y represents 1 influencing factor in an input layer of the neural network, namely the maximum value of the wall temperature of the vertical water wall; z represents the prediction output of the neural network, namely the prediction value of the wall temperature maximum value of the vertical water-cooled wall; n represents the number of the neural networks, namely the number of the predicted cycles; p represents the delay number of the input neuron of the neural network; x1(t-1),X1(t-2),…,X1(t-P) represents an input variable X11 st to P th historical data values of; y (t-1), Y (t-2), … Y (t-P) represents the 1 st to the P th historical data values of the input variable Y; z (1), Z (2), … Z (n) represents the predicted value of the maximum value of the wall temperature of the vertical water wall in the 1 st period to the nth period;
the number of hidden layer nodes of each neural network is represented by formula
Determining, wherein l is the number of nodes of an input layer, m is the number of nodes of an output layer, and a is a constant between 1 and 10; meanwhile, determining the delay order and the number of hidden layer nodes of the input variable according to model calculation; the input and output activation functions are all linear activation functions, so that the phenomenon that the wall temperature overtemperature point is weakened by a training model and the generalization capability is weakened can be avoided. Determining hidden layer weights by genetic algorithm, i.e. finding optimal adaptation by selection, crossing and mutation operationsThe degrees correspond to the individuals, so that the optimal weight of the hidden layer is determined.
The input neurons in each neural network are calculated by using the following transfer functions:
wherein: f is an activation function; w is aiThe weight value output to the neuron for the ith neuron of the upper layer; u. ofiThe output is the ith neuron of the upper layer, namely the ith input of the neuron; b is the bias of the neuron.
Compared with the prior art, the invention has the following advantages:
1) the neural network model not only considers key factors influencing the wall temperature of the water wall, namely unit load, coal quantity, primary air pressure, secondary air quantity and the like, but also considers historical data of the average temperature and the maximum value of the wall temperature of the water wall, and also considers the average wall temperature of an upstream heat exchanger, namely the average wall temperature of the spiral water wall, and the temperature of steam at the outlet of an economizer. The various inputs influencing the wall temperature of the water wall are used for forecasting, the temperature change of the upstream heat exchanger is considered, the uncertainty of randomly selecting variables is avoided, the input variables of the forecasting model are selected in a targeted mode, and a better forecasting result can be obtained.
2) The method has the advantages that a plurality of recursive neural network model structures are used, the prediction result is more accurate compared with the direct prediction of the maximum temperature of the vertical water-cooled wall after n periods, the advanced dynamic prediction of the wall temperature can be realized, the prediction result is utilized, the logical closed-loop control of the wall temperature overtemperature control is participated, sufficient operation time can be provided, the control is directly participated through part of work, the labor intensity of operators is effectively reduced, the monitoring work is effectively realized, meanwhile, the problem of safe operation such as pipe explosion caused by untimely wall temperature overtemperature reaction is avoided.
3) Determining an input variable structure, correcting an input parameter delay coefficient, the number of hidden layers and an activation function, and improving the training and generalization precision of the model.
Drawings
FIG. 1 is a schematic diagram of a neural network model for predicting wall temperature of a water wall of a coal-fired unit.
FIG. 2 is a water wall temperature prediction neural network model input delay coefficient determination curve.
FIG. 3 is a diagram of the effect of the water wall temperature prediction neural network model error.
Detailed Description
The present invention will be described in further detail with reference to the accompanying drawings and examples.
As shown in FIG. 1, the neural network model for predicting the wall temperature of the water wall of the coal-fired unit is formed by connecting a plurality of neural networks in a successive manner, wherein each neural network consists of an input layer, a hidden layer and an output layer; the input layer is divided into three types of input of a predicted heating surface wall temperature variable, an upstream heating surface wall temperature variable and other key variable, not only key factors influencing the wall temperature and the change condition of the upstream heating surface wall temperature are considered, but also the influence of the historical data of the predicted heating surface wall temperature on the input layer is considered; inputting the predicted heating surface wall temperature variable, namely the maximum value of the wall temperature of a vertical water-cooled wall of a certain wall of the boiler; the variable input of the wall temperature of the upstream heating surface refers to the temperature of the upper-stage heating surface or equipment of the vertical water-cooled wall, namely the average value of the steam temperature at the outlet of the economizer and the wall temperature of the spiral water-cooled wall; the other key variable inputs are parameters which obviously influence the wall temperature of the vertical water wall by the change of signals and comprise the actual load of a unit, the coal quantity, the primary air pressure and the secondary air flow; and the output layer is a predicted value of the maximum wall temperature of a vertical water-cooled wall of a certain wall of the boiler.
The input neurons of the first neural network in the plurality of neural networks comprise seven input neurons of the maximum wall temperature of a vertical water-cooled wall of a certain wall of a boiler, the temperature of steam at an outlet of an economizer, the average value of the wall temperature of a spiral water-cooled wall, the actual load of a unit, the coal quantity, the primary air pressure and the secondary air flow; the first neural network output neuron is a predicted value of the wall temperature maximum value of the vertical water-cooled wall in the prediction period 1; inputting and connecting a first neural network output neuron with a second neural network, sending a predicted value of the maximum value of the wall temperature of the vertical water-cooling wall in the prediction period 1 into a maximum value of the wall temperature of the vertical water-cooling wall in the second neural network and inputting the neuron into the neural network for updating historical data, connecting the rest input neurons with six input neurons of steam temperature at an outlet of an economizer, the average value of the wall temperature of the spiral water-cooling wall, the actual load of a unit, the coal quantity, primary air pressure and secondary air flow, and inputting and updating, wherein the second neural network output neuron is the predicted value of the maximum value of the wall temperature of the vertical water-cooling wall in the prediction period 2; analogizing in sequence to obtain a prediction result of the nth neural network output neuron, namely a prediction value of the wall temperature maximum value of the vertical water-cooling wall in the prediction period n; and the hidden layer neurons in the neural network model are respectively connected with all input neurons and all output neurons, normalized input variables of the input neurons of the neural network are multiplied by the weight values of all inputs, and the neuron output is obtained by calculating and superposing neuron bias through an activation function.
Example (b):
660MW unit of a power plant, the boiler is an ultra-supercritical once-through boiler, and a single hearth is adopted and reheated once. Comprehensively analyzing the wall temperature influence factors of the water wall at a certain point, and selecting the unit load, the coal quantity, the primary air pressure, the secondary air quantity, the average wall temperature of the upstream spiral water wall, the outlet temperature of the economizer and the maximum wall temperature value of the vertical water wall as the input layer of the neural network model.
The data sampling period is 10s, and six neural network models are used for output results after successive prediction is carried out for 60s, so that advanced dynamic prediction of the maximum value of the wall temperature is realized. Namely, the predicted value after 10s is used as the historical input of the maximum value of the water wall of the second neural network, so that the predicted value after 20s is given by the second neural network prediction, and the like, and the predicted result of the wall temperature of the water wall after 60s is obtained. According to the graph shown in fig. 2, model calculation determines that the delay order of the input variable is 20 to 22 to reach the optimum, the model selects the delay order of 20 and the number of hidden layer nodes is 4; the method comprises the following steps that (1) 7 input nodes are provided, each input node comprises 20 historical data, the number of hidden layer nodes is 4, the number of output layer nodes is 1, the maximum value of the wall temperature of the water wall is obtained, and the set neural network structure is 7-4-1; the input and output layer activation function is a linear function, so that the historical overtemperature point can not be weakened, and the generalization capability is stronger. Selecting 100000 groups of historical data of a certain power plant as training data to train a neural network, wherein the sampling period is 10s, and the deviation between a predicted value and an actual value is taken as an individual fitness value. The genetic algorithm finds out the individuals corresponding to the optimal individual fitness value through selection, intersection and variation operations; the obtained optimal individuals are assigned to the weight of the neural network, 10000 groups of historical data of a certain power plant are used as test data to predict the wall temperature, and the verification result shows that the advanced dynamic prediction of the wall temperature of the water cooling wall in advance by 60s can be realized, as shown in figure 3, the predicted average temperature deviation absolute value is only 0.3808 ℃, the predicted absolute error of more than 99% of the test points is within 1 ℃, the predicted maximum deviation is about 4 ℃, and the prediction model is accurate and effective.
Claims (5)
1. A coal-fired unit water-cooling wall temperature prediction neural network model is characterized in that: the device is composed of a plurality of neural networks which are connected in turn, wherein each neural network is composed of an input layer, a hidden layer and an output layer; the input layer is divided into three types of input of a predicted heating surface wall temperature variable, an upstream heating surface wall temperature variable and other key variable, not only key factors influencing the wall temperature and the change condition of the upstream heating surface wall temperature are considered, but also the influence of the historical data of the predicted heating surface wall temperature on the input layer is considered;
inputting the predicted heating surface wall temperature variable, namely the maximum value of the wall temperature of a vertical water-cooled wall of a certain wall of the boiler; the variable input of the wall temperature of the upstream heating surface refers to the temperature of the upper-stage heating surface or equipment of the vertical water-cooled wall, namely the average value of the steam temperature at the outlet of the economizer and the wall temperature of the spiral water-cooled wall; the other key variable inputs are parameters which obviously influence the wall temperature of the vertical water wall by the change of signals and comprise the actual load of a unit, the coal quantity, the primary air pressure and the secondary air flow; and the output layer is a predicted value of the maximum wall temperature of a vertical water-cooled wall of a certain wall of the boiler.
2. The coal-fired unit water wall temperature prediction neural network model of claim 1, characterized in that: the input neurons of the first neural network in the plurality of neural networks comprise seven input neurons of the maximum wall temperature of a vertical water-cooled wall of a certain wall of a boiler, the temperature of steam at an outlet of an economizer, the average value of the wall temperature of a spiral water-cooled wall, the actual load of a unit, the coal quantity, the primary air pressure and the secondary air flow; the first neural network output neuron is a predicted value of the wall temperature maximum value of the vertical water-cooled wall in the prediction period 1; inputting and connecting a first neural network output neuron with a second neural network, sending a predicted value of the maximum value of the wall temperature of the vertical water-cooling wall in the prediction period 1 into a maximum value of the wall temperature of the vertical water-cooling wall in the second neural network and inputting the neuron into the neural network for updating historical data, connecting the rest input neurons with six input neurons of steam temperature at an outlet of an economizer, the average value of the wall temperature of the spiral water-cooling wall, the actual load of a unit, the coal quantity, primary air pressure and secondary air flow, and inputting and updating, wherein the second neural network output neuron is the predicted value of the maximum value of the wall temperature of the vertical water-cooling wall in the prediction period 2; analogizing in sequence to obtain a prediction result of the nth neural network output neuron, namely a prediction value of the wall temperature maximum value of the vertical water-cooling wall in the prediction period n; and the hidden layer neurons in the neural network model are respectively connected with all input neurons and all output neurons, normalized input variables of the input neurons of the neural network are multiplied by the weight values of all inputs, and the neuron output is obtained by calculating and superposing neuron bias through an activation function.
3. The coal-fired unit water wall temperature prediction neural network model of claim 2, characterized in that: the mathematical relationship between the plurality of neural network output neurons and input neurons is represented by the following equation:
1 st neural network:
Z(1)＝f(X1(t-1)，X1(t-2)，…X1(t-P)，X2(t-1)，X2(t-2)，…X2(t-P)，…Y(t-1)，Y(t-2)，…Y(t-P))
the 2 nd neural network:
Z(2)＝f(X1(t-1)，X1(t-1)，X1(t-2)，…X1(t-P+1)，X2(t-1)，X2(t-1)，X2(t-2)，…X2(t-P+1)，…Z(1)，Y(t-1)，Y(t-2)，…Y(t-P+1))
…
the nth neural network:
wherein, X1、X2…X6Respectively representing 6 influencing factors in an input layer of the neural network, namely six inputs of steam temperature at an outlet of an economizer, an average value of wall temperature of a spiral water-cooled wall, actual load of a unit, coal quantity, primary air pressure and secondary air flow; y represents 1 influencing factor in an input layer of the neural network, namely the maximum value of the wall temperature of the vertical water wall; z represents the prediction output of the neural network, namely the prediction value of the wall temperature maximum value of the vertical water-cooled wall; n represents the number of the neural networks, namely the number of the predicted cycles; p represents the delay number of the input neuron of the neural network; x1(t-1)，X1(t-2)，…，X1(t-P) represents an input variable X11 st to P th historical data values of; y (t-1), Y (t-2), … Y (t-P) represents the 1 st to the P th historical data values of the input variable Y; z (1), Z (2), … Z (n) represents the predicted value of the wall temperature maximum value of the vertical water wall in the 1 st period to the P th period.
4. The coal-fired unit water wall temperature prediction neural network model of claim 1, characterized in that: the number of hidden layer nodes of each neural network is represented by formula
Determining, wherein l is the number of nodes of an input layer, m is the number of nodes of an output layer, and a is a constant between 1 and 10; meanwhile, determining the delay order and the number of hidden layer nodes of the input variable according to model calculation; the input and output activation functions are all linear activation functions, so that the phenomenon that the wall temperature overtemperature point is weakened by a training model and the generalization capability is weakened can be avoided. Determining the weight of the hidden layer through a genetic algorithm, namely searching an individual corresponding to the optimal fitness through selection, intersection and mutation operations, thereby determining the optimal weight of the hidden layer.
5. The coal-fired unit water wall temperature prediction neural network model of claim 1, characterized in that: the input neurons in each neural network are calculated by using the following transfer functions:
wherein: f is an activation function; w is aiThe weight value output to the neuron for the ith neuron of the upper layer; u. ofiThe output is the ith neuron of the upper layer, namely the ith input of the neuron; b is the bias of the neuron.
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