CN100362295C - Automatic volume regulating and controlling method for gas-burning machine heat pump - Google Patents
Automatic volume regulating and controlling method for gas-burning machine heat pump Download PDFInfo
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- CN100362295C CN100362295C CNB2006100132078A CN200610013207A CN100362295C CN 100362295 C CN100362295 C CN 100362295C CN B2006100132078 A CNB2006100132078 A CN B2006100132078A CN 200610013207 A CN200610013207 A CN 200610013207A CN 100362295 C CN100362295 C CN 100362295C
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Abstract
The present invention relates to an automatic capacity regulating and controlling method for a gas burning machine heat pump, which belongs to the capacity regulating technology of the gas burning machine heat pump. The regulating and controlling method comprises the steps that from macroscopic view, a mixed control system combining a plurality of control strategies of using the rotary speed of a gas burning machine as a regulating and controlling object, using a gas inlet valve of the gas burning machine as an actuating mechanism, using a building load prediction value as the main feedforward parameter and using the building room temperature as the controlled feedback parameter is used for carrying out regulation and control, and from microscopic view, by using the opening degree of an electronic expansion valve as the regulating and controlling object, using a stepping motor as the actuating mechanism, using the rotary speed of the gas burning machine as the feedforward parameter and using the superheat degree of the system as the feedback parameter, a mixed control system whose rotary speed and capacity control are similar to that of an engine is provided to carry out the regulation and control. The present invention has the advantages that the annual all-weather automatic operation capacity continuous regulation and the full automatic operation control of the heat pump unit are realized, and the indoor temperature stability is maintained to make the primary energy utilization ratio of the heat pump unit maintained at higher level in the whole operation period.
Description
Technical Field
The invention relates to a capacity automatic adjusting and controlling method of a building environment cold and hot combined supply total energy system by taking a plurality of selectable clean fuels such as natural gas or methane and the like as driving energy, in particular to a capacity automatic adjusting and controlling method for realizing full-year operation of cooling and hot water supply in summer, heating and hot water supply in winter by a gas engine heat pump system.
Background
In recent years, in many areas of China, the power shortage in summer is serious, and according to expert prediction, the power shortage condition in summer can not be effectively relieved within a plurality of years. The electric air conditioner is the main reason for forming peak load in summer, a gas engine driven cooling and heating combined supply unit basically does not need electric load, an electric driven air conditioning system can be replaced in summer, electric wave peak in summer is reduced, and the problems of cost, environment and the like in the selection of driving energy are effectively solved.
Gas engine heat pumps have the following advantages over electric heat pumps:
(1) Natural gas is used as primary energy, and the environmental performance of the gas engine heat pump is superior to that of an electric heat pump;
(2) The waste heat of the engine is recovered, and the heating performance and the defrosting performance of the gas engine heat pump are superior to those of an electric heat pump;
(3) The capacity is adjusted in a variable rotating speed mode, and the seasonal energy efficiency ratio is superior to that of a variable-frequency electric heat pump;
if the gas engine heat pump is used for cooling and heating a building, the cooling and heating capacity of the gas engine heat pump is required to be equal to the cooling and heating load of the building. It is known that the fluctuation range of the cold and heat load of a building is greatly influenced by factors such as environmental temperature change, personnel entering and exiting and the like. The gas engine heat pump system is a combined cycle consisting of a thermodynamic forward cycle of a gas engine and a thermodynamic reverse cycle of a heat pump unit, is a complex and comprehensive energy system, and has the factors of nonlinearity, time variation, pure hysteresis, strong coupling and the like. It can be seen that due to the complexity of the gas engine heat pump device, it is impossible to establish an accurate mathematical model, so that the classical control theory or the modern control theory lacks the most fundamental basis and is difficult to realize effective control. Although the self-adaptive and self-correcting control theory can carry out online identification on the controlled object lacking the mathematical model, the recurrence algorithm is responsible and has poor real-time performance. Therefore, the combination of the fuzzy control theory based on the language rule model and the neural network theory with self-learning and arbitrary approximation nonlinear mapping capability and the combination of the fuzzy control theory and the neural network theory with the proper classical control method become an effective path for solving the problems.
Therefore, on the premise that all parts forming the gas engine heat pump system are mature, the decisive factor for restricting the realization of independent intellectual property rights and commercial production of the gas engine heat pump system in China is the automatic capacity adjustment and control method.
Disclosure of Invention
The invention aims to provide an automatic capacity adjusting and controlling method for a gas engine heat pump, which has the characteristics of simple structure, convenience in operation, high control precision and safety and reliability in operation of an adopted adjusting and controlling system.
The invention is realized by the following technical scheme, a capacity regulating and controlling method of a gas engine heat pump, the gas engine heat pump system comprises a gas engine 1, a gas inlet valve 2, a cylinder sleeve heat exchanger 3 and a smoke exhaust heat exchanger 4; a transmission shaft 5 of the gas engine drives a compressor 7 to work through an electromagnetic clutch 6, an inlet and an outlet of the compressor are respectively connected with an evaporator 8 and a condenser 9, an electronic expansion valve 10 is arranged between the evaporator and the condenser, and a refrigeration system is formed by the compressor, the evaporator, the condenser and the electronic expansion valve.
This regulation and control system of gas engine heat pump includes: the method for adjusting and controlling the capacity of the gas engine heat pump by adopting the adjusting and controlling system is characterized by comprising the following steps:
1. macroscopically using the rotating speed of the gas engine as a regulation object, using an air inlet valve stepping motor of the gas engine as an actuating mechanism, using a predicted value of building load as control feedforward information of a main parameter, and using the indoor temperature of the building as control feedback information, the process for realizing the regulation and control of the heat pump capacity of the gas engine comprises the following steps:
1) On the basis of improving a nearest neighbor cluster learning algorithm, a weighted dynamic RBF prediction network model is provided, the annual air temperature, solar radiation and building historical load parameters of the location of the building are used as training samples of a neural network model, the cold and hot load required by the building at the next moment is predicted, and the load is used as a feedforward input parameter of the system.
The algorithm and model are as follows:
(1) RBF neural network:
the RBF radial basis function neural network is a forward neural network and comprises an implicit layer, and the mathematical model of the RBF radial basis function neural network is as follows:
wherein x ∈ R n Inputting a neural network;
w i outputting layer weight for RBF;
g is a radial basis function;
c i is the center of the radial basis function;
σ i for the radial basis function receptive field (sensitive domain), σ i The larger the receptive field is;
‖‖ Rn is x and c i The distance between them.
(2) Improved nearest neighbor clustering algorithm
a. Selecting a proper radius r to define a vector A u For storing the sum of output vectors belonging to classes, defining a counter B u And the method is used for counting the number of samples belonging to each type.
b. From the first data pair (x) 1 ，y 1 ) At the beginning, at x 1 A cluster center is established, order
The weight vector from the hidden unit to the output layer is
x 1 ＝A 1 /B 1
c. Suppose that the kth sample data pair (x) is considered k ，y k ) K =2,3, N has M cluster centers, the center points of which are divided intoIs otherwise c 1 ，c 2 ，...，c M . Then, the distances | x from the M cluster centers are respectively obtained k -c j I =1,2,3,. Lam, M, let | x k -c j I is the smallest of these distances, i.e. c j Is x k Then:
if | x k -c j If | > r, then x k As a new oneCluster centers and order
And hold A i ，B i Is constant, i =1,2, 3.
If | x k -c j And (5) r is less than or equal to r, and the calculation is carried out as follows:
when i ≠ j, i =1,2,3,. Lam, M, and holds a i ，B i The value of (c) is not changed. The weight vector from the hidden unit to the output layer is:
wi＝Ai/Bi，i＝1，2，3，...，M
(3) weighted RBF neural network
The output of the weighted RBF network established according to the above rules should be:
where σ is the width of the base function receptive field, σ = r can be taken, which is much more convenient than determining the number of hidden units and a suitable norm simultaneously. Meanwhile, each input/output data pair can generate a new cluster, so that the dynamic self-adaptive RBF network actually performs self-adaptive adjustment of two processes of parameters and structure.
(4) Load prediction network model
And establishing a load prediction network model according to the temperature of the place where the building is located, solar radiation, humidity, wind speed and surface temperature, the structural characteristics of the building and the geographical position.
2) The central control link adopts a fuzzy control system and a PID dual-mode parallel control mode, and introduces a Smith pre-estimation compensator on the basis of fuzzy PID.
The algorithm and model are as follows:
(1) a fuzzy controller:
it has three features:
a. fuzzification: converting the accurate input quantity into a fuzzy input quantity;
b. fuzzy reasoning: obtaining fuzzy control quantity according to the control rule and the input fuzzy quantity;
c. defuzzification: converting the fuzzy output into accurate output;
(2) PID control system:
under the action of PID controller, proportional, integral and differential operations are respectively carried out on error signal e (t) of building indoor temperature, and the weighted sum of the results forms control signal u (t) of system, which is transmitted to the stepping motor of actuating mechanism gas inlet valve for control. The mathematical description of the PID controller is:
(3) fuzzy-PID controller
The dual-mode controller is adopted, when the deviation of the indoor temperature of the building is large, the fuzzy controller is adopted when the deviation exceeds a certain threshold value, and when the deviation of the indoor temperature of the building is small, the fuzzy controller is switched to the PID controller when the deviation is smaller than the threshold value, so that the dual-mode controller has the dual characteristics of quick response of the fuzzy controller and high steady-state precision of the PID controller;
(4) smith predictor
And introducing the fuzzy controller into a Smith estimation control system to form a Smith fuzzy control system.
3) Comparing the feedback information with the setting information: when the indoor temperature of the building is higher than the set indoor temperature, the controller calculates and sends out an instruction through a control algorithm, so that the stepping motor rotates forwards for a specific number of steps, the opening of the gas inlet valve of the gas engine is increased, the gas inlet amount is increased, the rotating speed of the gas engine is increased, the rotation of the compressor is accelerated, the flow of the refrigerant in each cycle is increased, the total refrigerating capacity of the heat pump is increased, the indoor temperature is gradually reduced, and the temperature is close to the set temperature. When the indoor temperature of the building is lower than the set indoor temperature, the controller calculates and sends out an instruction through a control algorithm, so that the stepping motor rotates reversely for a specific step number, the opening degree of an air inlet valve of the gas engine is reduced, the gas inlet amount is reduced, the rotating speed of the gas engine is reduced, the rotation of the speed reducing compressor is reduced, the flow of the refrigerant in each cycle is reduced, the total refrigerating capacity of the heat pump is reduced, the indoor temperature is gradually reduced, and the temperature is close to the set temperature.
2. Microcosmically using the opening of an electronic expansion valve as a regulation object, using a stepping motor of the electronic expansion valve as an actuating mechanism, using the rotating speed of a gas engine as feedforward information and using the degree of superheat of the system as feedback information, and providing a hybrid control system combining a plurality of control strategies similar to the variable engine rotating speed capacity control, comprising:
1) The feedforward part uses the engine speed as a main input parameter;
2) The feedback part adopts a fuzzy control system and a PID dual-mode parallel control mode, and introduces a Smith pre-estimation compensator on the basis of fuzzy PID; a feedback link enables an actual superheat degree and a set superheat degree to form deviation, a dual-mode controller is adopted, when the superheat degree deviation is large, a fuzzy controller is adopted when the superheat degree deviation exceeds a certain threshold value, when the superheat degree deviation is small, the fuzzy controller is switched to a PID controller when the superheat degree deviation is smaller than the threshold value, control quantity is output to an electronic expansion valve stepping motor, the actual superheat degree of a system is enabled to continuously approach the set superheat degree, namely when the actual superheat degree of the system is high, the actual superheat degree of the system is enabled to continuously reduce and approach the set superheat degree; otherwise, the temperature is continuously raised and approaches the set superheat degree.
The beneficial effects of the invention are as follows: the capacity continuous adjustment and full-automatic operation control of the heat pump unit all-weather automatic operation all the year around are realized, the indoor temperature is maintained to be stable, and the primary energy utilization rate of the heat pump unit is kept at a higher level in the whole operation period.
Drawings
FIG. 1 is a diagram of a radial basis function neural network model architecture;
FIG. 2 is a block diagram of a load prediction network model;
FIG. 3 is a schematic block diagram of a two-dimensional fuzzy control system;
FIG. 4 is a block diagram of a gas engine heat pump system with a control point;
FIG. 5 is a functional block diagram of a variable engine speed capacity control;
FIG. 6 is a block diagram of a dual mode control system architecture for fuzzy PID;
FIG. 7 is a block diagram of a Smith fuzzy PID control system;
fig. 8 is a schematic block diagram of electronic expansion valve capacity modulation control.
In the figure:
1-gas engine 2-throttle valve 3-cylinder sleeve heat exchanger
4-smoke exhausting heat exchanger 5-transmission shaft 6-clutch
7-compressor 8-evaporator 9-condenser
10-electronic expansion valve 11-controller 12-throttle stepping motor
13-electronic expansion valve stepping motor.
Detailed Description
The following description of the invention, with reference to fig. 4, is provided:
the embodiment is the running condition of a cold and hot water unit of a gas engine heat pump, and the capacity adjusting mode is that the temperature of supplied water is changed for fixed water supply flow. The working process of the embodiment is as follows:
1) Running in winter
Taking the working condition of a certain day in winter as an example, the building is started in the morning, the outdoor temperature is 0 ℃, the thermal load required by the building at the next moment is predicted to be 40kW according to the weighted dynamic RBF neural network, and the load is used as feed-forward input. Then, the temperature sensor 15 detects the outlet water temperature of the heat pump unit, which is assumed to be 20 ℃ at present. The temperature of 20 ℃ and the set outlet water temperature of the unit (when the heating mode is operated, the outlet water temperature is set to be 60 ℃) form a deviation of-40 ℃, then a PID controller or a fuzzy controller is judged to be adopted in the controller 11 according to the deviation value, the fuzzy controller is adopted when the deviation is larger than a judgment threshold value, firstly the temperature value and the temperature deviation value are converted into fuzzy input quantity, then the fuzzy control quantity of the engine rotating speed is calculated according to a fuzzy control rule, then the fuzzy quantity is converted into accurate quantity output, finally the engine rotating speed is obtained to be increased, the increment is 500 revolutions per minute, the increment is firstly converted into the increment of the opening degree of an air inlet valve of the gas engine, then is converted into the forward rotation step number of the stepping motor of the air inlet valve, and the step number is 100 steps. The specific operation process is as follows: the stepping motor 12 of the gas inlet valve of the gas engine rotates forwards for 100 steps, the opening degree of the gas inlet valve 2 is increased, the air inflow of the gas engine 1 is further increased, and finally the rotating speed of the gas engine 1 is increased by 500 revolutions per minute; the increase of the rotating speed is transmitted to the compressor 7 through the transmission shaft 5 and the clutch 6, so that the circulating flow of the refrigerant is increased, the heating capacity of the heat pump unit is increased, and the outlet water temperature of the heat pump unit is gradually increased from 20 ℃.
Meanwhile, the superheat degree of the system (namely the difference value of the refrigerant outlet temperature 16 of the evaporator 8 and the refrigerant inlet temperature 17 of the evaporator) is detected, the superheat degree is assumed to be 12 ℃ and set superheat degree 8 ℃ to form a deviation of 4 ℃, then a PID controller or a fuzzy controller is judged in the controller 11 according to the deviation value, the fuzzy controller is adopted when the deviation is larger than a judgment threshold value, firstly, the superheat degree and the superheat degree deviation value are converted into fuzzy input quantity, then, a fuzzy control quantity of the opening degree of the electronic expansion valve is obtained through calculation according to a fuzzy control rule, then, the fuzzy quantity is converted into an accurate quantity output quantity, finally, an output quantity, namely, the opening degree increment of the electronic expansion valve 10 is obtained, the electronic expansion valve stepping motor 13 is controlled to rotate forwards for 10 steps, the opening degree of the electronic expansion valve 10 is increased, the increase of the circulating refrigerant flow caused by the increase of the rotating speed of the compressor 6 is met, the superheat degree is gradually reduced from 12 ℃ to the set superheat degree to be close to the set superheat degree 8 ℃, the stable operation of the evaporator 8 ℃ can be ensured to be always under the set superheat degree, and the dynamic characteristic of the system variable load is greatly improved.
With the lapse of time, the system gradually enters a stable state, the outlet water temperature of the heat pump unit reaches the set 60 ℃, and the superheat degree also reaches the set 8 ℃. At the moment, if the temperature begins to be reduced outdoors, if the temperature is reduced from 0 ℃ to-10 ℃ from the beginning, the heat load required by the building begins to increase along with the change of the cold weather, at the moment, the outlet water temperature of the heat pump unit cannot be maintained at 60 ℃, the temperature begins to be gradually reduced to 50 ℃, the indoor temperature also decreases along with the change of the cold weather, at the moment, the heating capacity of the system must be increased to meet the requirement of the increase of the heat load of the building, otherwise, the stability of the indoor temperature cannot be maintained, and the normal work, study and life of people are influenced.
The controller 11 is responsible for increasing the system heating capacity, and the specific implementation manner is as follows: the controller 11 obtains the thermal load required by the building through the prediction of a weighted dynamic RBF neural network, the thermal load is 50kW, the deviation of the current outlet water temperature is minus 10 ℃ (50 ℃ -60 ℃), then whether a PID controller or a fuzzy controller is adopted is judged in the controller 11 according to the deviation value, the deviation is smaller than a judgment threshold value at the moment, the PID controller is adopted, and the deviation is calculated according to a formula
The required increment of the engine speed is calculated to be 100 rpm, and the increment is converted into the step number of the positive rotation of the stepping motor of the air inlet valve to be 20 steps. The air inlet valve stepping motor 12 rotates forwards for 20 steps, the opening degree of the air inlet valve 2 is increased, the air inflow of the gas engine 1 is increased, and finally the rotating speed of the gas engine 1 is increased by 100 r/m, so that the circulating flow of the refrigerant is increased, the heating capacity of the heat pump unit is increased, the water outlet temperature of the heat pump unit is gradually increased from 50 ℃ and finally reaches 60 ℃ which is set, and the gas engine operates in a new stable state. (variable speed regulation with electronic expansion valve capacity regulation throughout, the regulation process is the same as above.)
2) Operation in summer
In summerIn the case of one-day operation, for example, the building is started in the morning, the outdoor temperature is 35 ℃, the cold load required by the building at the next moment is predicted to be 50kW according to the weighted dynamic RBF neural network, and the load is used as a feed-forward input. Then, the temperature sensor 15 detects the outlet water temperature of the heat pump unit, which is assumed to be 20 ℃ at present. The temperature of 20 ℃ and the set water outlet temperature of the unit (when the unit operates in a refrigeration mode, the set water outlet temperature is 7 ℃) form a deviation of 13 ℃, and then a PID controller or a fuzzy controller is adopted in the controller 11 according to the deviation value, and the deviation is smaller than a judgment threshold value and PID control is adoptedDevice according to formula
The engine speed is calculated to need to be increased by 200 rpm, which is converted first to an amount of increase in the opening of the gas engine intake valve and then to the number of steps of forward rotation of the intake valve stepper motor, which is 40 steps. The specific operation process is as follows: the gas engine inlet valve step motor 12 rotates forward for 40 steps, the opening degree of the inlet valve 2 is increased, the air inflow of the gas engine 1 is further increased, and finally the rotating speed of the gas engine 1 is increased by 200 revolutions per minute; the increase of the rotating speed is transmitted to the compressor 7 through the transmission shaft 5 and the clutch 6, so that the circulating flow of the refrigerant is increased, the refrigerating capacity of the heat pump unit is increased, and the outlet water temperature of the heat pump unit is gradually reduced from 20 ℃.
Meanwhile, the superheat degree of the system (namely the difference value between the refrigerant outlet temperature of the evaporator 8 and the refrigerant inlet temperature of the evaporator) is detected, the superheat degree is assumed to be 5 ℃ and is set to be 8 ℃ to form a deviation of-3 ℃, then a PID controller or a fuzzy controller is adopted in the controller 11 according to the deviation value, the PID controller is adopted when the deviation is smaller than a judgment threshold value, and the PID controller is adopted according to a formula
Calculating to obtain the output quantity, i.e. the opening degree reduction quantity of the electronic expansion valve 10, and controlling the electronic expansion valve stepping motor 13 to rotate reversely for 8 steps to reduce the opening degree of the electronic expansion valve 10 so as to meet the requirement of the flow quantity increase of the circulating refrigerant caused by the rotation speed increase of the compressor 6And moreover, the superheat degree is gradually increased from 5 ℃ to 8 ℃ close to the set superheat degree, so that the evaporator 8 can be ensured to stably work under the set superheat degree all the time, and the dynamic characteristic of the variable load of the system is greatly improved.
With the lapse of time, the system gradually enters a stable state, the outlet water temperature of the heat pump unit reaches the set 7 ℃, and the superheat degree also reaches the set 8 ℃. At this time, if the outdoor temperature begins to be reduced, if the temperature is reduced from 35 ℃ to 30 ℃, the cold load required by the building begins to be reduced along with the cooling of the weather, at this time, the outlet water temperature of the heat pump unit also begins to be gradually reduced to 5 ℃, the indoor temperature also reduces along with the reduction, at this time, the refrigerating capacity of the system needs to be reduced to meet the requirement of reducing the cold load of the building, otherwise, the indoor temperature cannot be maintained to be stable, and the normal work, study and life of people are influenced.
The controller 11 is responsible for reducing the cooling capacity of the system, and the specific implementation manner is as follows: the controller 11 obtains the cooling load required by the building and the deviation of the current outlet water temperature of-2 ℃ (5 ℃ -7 ℃) through the prediction of a weighted dynamic RBF neural network, then the controller 11 judges whether a PID controller or a fuzzy controller is adopted according to the deviation value, the deviation is smaller than a judgment threshold value, the PID controller is adopted according to a formula, and the PID controller is adopted according to the formula
And calculating to obtain the required reduction of the engine speed as 30 revolutions per minute, and converting the required reduction into 5 steps of reverse rotation steps of the air inlet valve stepping motor. The air inlet valve stepping motor 12 rotates reversely for 20 steps, the opening degree of the air inlet valve 2 is reduced, the air inflow of the gas engine 1 is further reduced, and finally the rotating speed of the gas engine 1 is reduced by 30 r/m, so that the circulating flow of the refrigerant is reduced, the refrigerating capacity of the heat pump unit is reduced, the water outlet temperature of the heat pump unit is gradually increased from 5 ℃ and finally reaches the set 7 ℃, and the heat pump unit operates in a new stable state. (the variable speed regulation process is accompanied by the capacity regulation of the electronic expansion valve all the time, and the regulation process is the same as the above.)
Claims (1)
1. A capacity regulation and control method of the heat pump of the gas engine, the heat pump system of the said gas engine, including the gas engine (1), its gas inlet valve (2), its cylinder liner heat exchanger (3), its exhaust gas heat exchanger (4); a transmission shaft (5) of the gas engine drives a compressor (7) to work through an electromagnetic clutch (6), an inlet and an outlet of the compressor are respectively connected with an evaporator (8) and a condenser (9), an electronic expansion valve (10) is arranged between the evaporator and the condenser, and the compressor, the evaporator, the condenser and the electronic expansion valve form a refrigeration system; this regulation and control system of gas engine heat pump includes: the system for acquiring data of sensors of temperature, pressure and rotating speed, the controller (11), the stepping motor (13) of the electronic expansion valve and the actuating mechanism of the stepping motor (12) of the fuel gas inlet valve, and the method for adjusting and controlling the capacity of the heat pump of the fuel gas engine by adopting the regulation and control system is characterized by comprising the following processes:
1) Macroscopically using the rotating speed of the gas engine as a regulation object, using an air inlet valve stepping motor of the gas engine as an actuating mechanism, using a predicted value of building load as control feedforward information of a main parameter, and using the indoor temperature of the building as control feedback information, and realizing the capacity regulation and control process of the heat pump of the gas engine comprises the following steps:
(1) On the basis of improving a nearest neighbor cluster learning algorithm, a weighted dynamic RBF prediction network model is provided, the annual hourly air temperature, solar radiation and building historical load parameters of the location of a building are used as training samples of a neural network model, the cold and hot load required by the building at the next moment is predicted, and the load is used as a feedforward input parameter of a system;
the algorithm and model are as follows:
(1) RBF neural network:
the RBF radial basis function neural network is a forward neural network and comprises an implicit layer, and the mathematical model of the RBF radial basis function neural network is as follows:
wherein x ∈ R n Is a neural netInputting a collateral;
w i outputting layer weight for RBF;
g is a radial basis function;
c i is the center of the radial basis function;
σ i is the radial basis function receptive field, σ i The larger the receptive field is;
‖‖ Rn is x and c i The distance between them;
(2) improved nearest neighbor clustering algorithm:
a. selecting a proper radius r to define a vector A u For storing the sum of output vectors belonging to various classes, defining a counter B u The method is used for counting the number of samples belonging to each type;
b. from the first data pair (x) 1 ，y 1 ) At the beginning, at x 1 A cluster center is established, order
The weight vector from the hidden unit to the output layer is
x 1 ＝A 1 /B 1
c. Suppose that the kth sample data pair (x) is considered k ，y k ) When k =2,3, N has M cluster centers with a center point c 1 ，c 2 ，...，c M Then, the distances | x to the M cluster centers are respectively obtained k -c j I =1,2, 3., M, let | x k -c j I is the minimum of these distances, i.e. c j Is x k Then:
if | x k -c j If | is greater than r, then x is k As a new cluster center, and
and hold A i ，B i Is constant, i =1,2,3, ·, M-1;
if | x k -c j L is less than or equal to r, and the calculation is carried out as follows:
when i ≠ j, i =1,2,3,. Eta, M, and remains a i ，B i The value of (a) is unchanged, and the weight vector from the hidden unit to the output layer is:
wi＝Ai/Bi，i＝1，2，3，...，M
(3) weighted RBF neural network:
the output of the weighted RBF network established according to the above rules should be:
the dynamic self-adaptive RBF network actually carries out self-adaptive adjustment of two processes of parameters and structure because each input and output data pair can generate a new cluster;
(4) load prediction network model:
establishing a load prediction network model according to the temperature, solar radiation, humidity, wind speed and ground surface temperature of the place where the building is located, the structural characteristics and the geographical position of the building;
(2) The central control link adopts a fuzzy control system and a PID dual-mode parallel control mode, and introduces a Smith pre-estimation compensator on the basis of fuzzy PID;
the algorithm and model are as follows:
(1) a fuzzy controller:
it has three features:
a. fuzzification: converting the accurate input quantity into a fuzzy input quantity;
b. fuzzy reasoning: obtaining fuzzy control quantity according to the control rule and the input fuzzy quantity;
c. defuzzification: converting the fuzzy output into accurate output;
(2) PID control system:
under the action of a PID controller, proportional, integral and differential operations are respectively carried out on an error signal e (t) of the indoor temperature of the building, the weighted sum of the results forms a control signal u (t) of a system, the control signal u (t) is transmitted to an actuating mechanism to be controlled, and the mathematical description of the PID controller is as follows:
(3) fuzzy-PID controller:
the dual-mode controller is adopted, when the deviation of the indoor temperature of the building is large, the fuzzy controller is adopted when the deviation exceeds a certain threshold value, and when the deviation of the indoor temperature of the building is small, the fuzzy controller is switched to the PID controller when the deviation is smaller than the threshold value, so that the dual-mode controller has the dual characteristics of quick response of the fuzzy controller and high steady-state precision of the PID controller;
(4) smith predictor:
introducing the fuzzy controller into a Smith pre-estimation control system to form a Smith fuzzy control system;
(3) Comparing the feedback information with the setting information: when the indoor temperature of a building is higher than the set indoor temperature, the controller calculates and sends an instruction through a control algorithm, so that the stepping motor rotates forwards for a specific number of steps, the opening of an air inlet valve of the gas engine is increased, the air inflow of gas is increased, the rotating speed of the gas engine is increased, the rotation of the compressor is accelerated, the flow of refrigerant in each cycle is increased, the total refrigerating capacity of the heat pump is increased, the indoor temperature is gradually reduced, and the temperature is close to the set temperature; when the indoor temperature of a building is lower than the set indoor temperature, the controller calculates and sends an instruction through a control algorithm, so that the stepping motor rotates reversely for a specific step number, the opening degree of an air inlet valve of the gas engine is reduced, the gas inlet amount is reduced, the rotating speed of the gas engine is reduced, the rotation of the compressor is reduced, the flow of refrigerant in each cycle is reduced, the total refrigerating capacity of the heat pump is reduced, the indoor temperature is gradually reduced, and the temperature is close to the set temperature;
2) The microcosmic hybrid control system which takes the opening degree of an electronic expansion valve as a regulating object, takes a stepping motor of the electronic expansion valve as an actuating mechanism, takes the rotating speed of a gas engine as feedforward information and takes the degree of superheat of the system as feedback information and combines various control strategies comprises the following steps:
(1) The feedforward part uses the engine speed as a main input parameter;
(2) The feedback part adopts a fuzzy control system and a PID dual-mode parallel control mode, and introduces a Smith pre-estimation compensator on the basis of fuzzy PID; a feedback link enables an actual superheat degree and a set superheat degree to form deviation, a dual-mode controller is adopted, when the superheat degree deviation is large, a fuzzy controller is adopted when the superheat degree deviation exceeds a certain threshold value, when the superheat degree deviation is small, the fuzzy controller is switched to a PID controller when the superheat degree deviation is smaller than the threshold value, control quantity is output to an electronic expansion valve stepping motor, the actual superheat degree of a system is enabled to continuously approach the set superheat degree, namely when the actual superheat degree of the system is higher, the actual superheat degree of the system is enabled to continuously decrease and approach the set superheat degree; otherwise, the temperature is continuously raised to approach the set superheat degree.
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| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CNB2006100132078A CN100362295C (en) | 2006-02-22 | 2006-02-22 | Automatic volume regulating and controlling method for gas-burning machine heat pump |
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| CN101286044B (en) * | 2008-05-12 | 2010-06-16 | 杭州电子科技大学 | Coal-burning boiler system steam-temperature mixing modeling method |
| JP4995169B2 (en) | 2008-09-29 | 2012-08-08 | 三菱重工業株式会社 | Gas turbine control method and apparatus |
| CN101556458B (en) * | 2009-05-19 | 2011-02-09 | 江汉大学 | Automatic control algorithm for feeding vitriol in tap water by coagulation |
| CN101737171B (en) * | 2009-12-23 | 2011-11-30 | 电子科技大学 | Aeroengine generation control device |
| JP5607501B2 (en) * | 2010-11-08 | 2014-10-15 | 株式会社堀場エステック | Mass flow controller |
| CN102087531B (en) * | 2011-02-21 | 2012-12-12 | 莱芜钢铁集团电子有限公司 | Liquid pump flow control method |
| CN102418518A (en) * | 2011-04-12 | 2012-04-18 | 北京师范大学 | Method for identifying water logging grades of oil reservoir by using neural network analogue cross plot |
| CN103123460A (en) * | 2011-11-21 | 2013-05-29 | 才秀君 | Temperature control system and temperature control method |
| CN103423836B (en) * | 2012-04-24 | 2018-03-13 | 杭州三花研究院有限公司 | Vehicle air conditioner control method for overheat and vehicle air conditioner |
| JP2014163594A (en) * | 2013-02-26 | 2014-09-08 | Mitsubishi Electric Corp | Flow control device and fluid circuit system |
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| WO2016144929A1 (en) | 2015-03-09 | 2016-09-15 | Carrier Corporation | Expansion valve control |
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| CN106904058B (en) * | 2017-03-10 | 2019-12-31 | 安徽江淮汽车集团股份有限公司 | Control method and device for electronic expansion valve in heat pump air conditioning system |
| CN107401787A (en) * | 2017-06-22 | 2017-11-28 | 天津城建大学 | Improve the control system and control method of gas heat pump air-conditioning stability |
| CN110523043A (en) * | 2018-05-25 | 2019-12-03 | 中国科学院沈阳自动化研究所 | Spherical tank fire disaster simulation burner and control method |
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| CN109976405A (en) * | 2019-03-12 | 2019-07-05 | 厦门理工学院 | A kind of ceramic kiln temperature composite control method, equipment and system |
| CN111001119B (en) * | 2019-10-16 | 2021-12-14 | 国网浙江省电力有限公司嘉兴供电公司 | Intelligent gas fire extinguishing system and method for multi-station fusion power machine room |
| CN112163703B (en) * | 2020-09-25 | 2024-02-02 | 中国水利水电科学研究院 | Farmland reference crop evapotranspiration prediction method considering weather factor uncertainty |
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