CN106382668A - System and method capable of realizing heating by combining electric heat storage boiler with solar energy - Google Patents

Abstract

The invention provides a system and method for combined heating of an electric heat storage boiler and solar energy, and relates to the technical field of heating. The system includes solar heat collection unit, heat storage unit, electric boiler unit, user pipe network unit and system control unit. The BP neural network is used to optimize the PID control parameters, and the particle swarm algorithm is used to optimize it. The control unit controls the optimal PID control The parameter is transformed into a signal to control the opening of the fourth solenoid valve, and the valve opening of the fourth solenoid valve is adjusted according to the deviation between the actual temperature and the set stable temperature value, so as to achieve the effect of adjusting the indoor temperature. The system of the present invention has a compact and reasonable structure, can form energy complementarity in the whole heating period, realize relatively stable joint heating, adopt the control method of PSO‑BP neural network to obtain the optimal PID control parameters, and can make full use of low-valley electric energy and improve power grid The load rate can also make up for the shortcomings of the solar heating system when it is used alone, effectively ensuring the heating quality.

Description

System and method for combined heating of electric heat storage boiler and solar energy

Technical field:

The invention relates to the technical field of heating, in particular to a combined heating system and method of an electric heat storage boiler and solar energy.

Background technique:

At present, smog has become a common phenomenon of urban pollution. In northern cities, coal is mainly used for heating in winter, and the proportion of clean heating is low. Among them, coal-fired heating is the most important source of pollution. In areas such as urban central heating, the use of solar energy for heating can reduce pollutant emissions. However, solar energy is easily affected by factors such as seasons, climates, and locations. The heating efficiency is low and unstable, which makes solar heating systems have great limitations. . The general electric boiler has no emission and no pollution to the environment, but it uses electricity for heating day and night, which will lead to an increase in expenses, and there will be a situation where equipment power cannot be supplied during peak power supply hours. Based on this, it is necessary to study the combination of solar energy and other new energy sources with electric heat storage boilers for heating.

In order to stabilize the temperature on the heating user side, it is necessary to control the temperature of the combined heating system of the solar electric thermal storage boiler. The current heating system temperature control methods often use simple PID control, adaptive control, fuzzy control, neural network control, etc., because the temperature control has the characteristics of large inertia and pure hysteresis, and the above-mentioned control method considers a single influencing factor, it is very difficult for the combined heating system. The control effect is not ideal.

Invention content:

Aiming at the defects of the prior art, the present invention provides a system and method for combined heating of an electric heat storage boiler and solar energy. Temperature influence factors, use BP neural network to optimize the control parameters of PID controller, and use Particle Swarm Optimization (PSO) to optimize and improve it, which can not only make full use of low-valley power, increase the load rate of the power grid, but also make up for When the solar heating system is used alone, it has the disadvantage of insufficient heating at night or in continuous rainy days, so as to realize the efficient use of solar energy and effectively ensure the quality of heating.

On the one hand, the present invention provides a combined heating system of an electric heat storage boiler and solar energy, including a solar heat collection unit, a heat storage unit, an electric boiler unit, a user pipe network unit, and a system control unit; a solar heat collection unit and an electric boiler unit It is used to provide heating source for the system, which is the energy supply part of the system, and is connected to the heat storage unit; the heat storage unit is used to store hot water, and is the energy storage part of the system, connected to the user pipe network unit; the user pipe network unit is used as the system circulation The channel and heating end; the system control unit is used to control each unit to ensure the automatic operation of the system;

The solar heat collection unit includes a solar heat collector, a heat exchanger, a first temperature sensor, a first circulating water pump and a first solenoid valve; the solar heat collector is used to continuously heat the water in it under sunlight, and the solar heat collector The water inlet is connected to the cold water pipe; the heat exchanger is used for heat exchange with the solar collector; the first temperature sensor is set in the water outlet of the solar collector to detect the water temperature at the outlet of the solar collector; the first cycle The water pump and the first solenoid valve are sequentially connected to the end of the water outlet of the solar collector, used to open the water outlet of the solar collector, and extract the hot water in the solar collector to the heat exchanger through the user pipe network unit;

Electric boiler unit, including electric thermal storage boiler, second temperature sensor, second circulating water pump, second solenoid valve and power grid switching device; electric thermal storage boiler is used for operation during the low power consumption period at night and extreme weather under normal conditions During the non-low-peak power consumption period, the water in it is heated, and the water inlet of the electric heat storage boiler is connected to the cold water pipe; the second temperature sensor is installed in the water outlet of the electric heat storage boiler to detect the temperature at the water outlet of the electric heat storage boiler. Water temperature; the second circulating water pump and the second solenoid valve are connected to the end of the water outlet of the electric heat storage boiler in sequence, and are used to open the water outlet of the electric heat storage boiler, and extract the hot water in the electric heat storage boiler through the user pipe network unit to Heat storage unit; the grid switching device is connected to the grid power supply to supply power for the operation of the electric heat storage boiler;

The heat storage unit includes the heat storage tank, the third circulating water pump, the third solenoid valve, the fourth circulating water pump and the fourth solenoid valve; the heat storage tank includes the insulation layer, the liquid level sensor at the inner bottom and the third The temperature sensor, the liquid level sensor is used to detect the water level inside the hot water storage tank, and the third temperature sensor is used to detect the water temperature at the water outlet of the hot water storage tank; the third circulating water pump and the third solenoid valve are connected to the hot water storage tank in sequence The upper end of the water inlet of the heat exchanger is used to extract the hot water of the heat exchanger to the heat storage unit through the user pipe network unit; the fourth circulating water pump and the fourth solenoid valve are connected to the end of the water outlet of the heat storage tank in turn to open the heat storage unit. The water outlet of the hot water tank draws the hot water from the hot water storage tank to the heating end of the user for heating;

User pipe network unit, including circulation pipes, floor heating coils, water collectors and water distributors; circulation pipes are used to connect the waterways between units in the system; floor heating coils are heating terminals for heating users; water collection The water separator is set at the water inlet of the floor heating coil, and is connected to the heat storage unit through the circulation pipe; the water separator is set at the water outlet of the floor heating coil, and is connected with the water inlet of the solar collector and the electric heat storage boiler through the circulation pipe;

System control unit, including indoor temperature sensor, central controller and its peripheral control circuit. The indoor temperature sensor is used to collect the temperature in the user's room, and the central controller is used to receive the signals of each temperature sensor and liquid level sensor, and send out control The signal is used to control the opening of each solenoid valve, the operation of each circulating water pump and the operation of the power grid switching device through the peripheral control circuit.

Further, the circulation pipeline adopts copper pipe; the floor heating coil adopts coiled water pipe arranged in a circle; the central controller is a single-chip microcomputer.

On the other hand, the present invention also provides a method for combined heating of an electric heat storage boiler and solar energy, which is realized by using the above-mentioned combined heating system of an electric heat storage boiler and solar energy, and the specific steps are as follows:

Step 1. Initialize various settings of the system, including setting the temperature value of each temperature sensor, setting the pressure value of the liquid level sensor, setting the temperature stability value in the user's room, initializing the topology of the BP neural network and initializing the particle swarm parameters; Set the BP neural network to adopt a three-layer structure, in which the input layer is set to 3 neurons, representing input, output and error respectively;

Step 2. Collect the indoor temperature in real time, obtain the actual temperature r _in (k) of the indoor temperature sensor, the stable value of the indoor temperature y _out (k) set by the user, and the deviation e(k) between the actual temperature and the set stable temperature value ), e(k)=y _out (k)-r _in (k);

Step 3, with r _in (k), y _out (k), e (k) as the input layer of input signal input BP neural network, calculate the PID control parameter of output layer;

Step 4. Obtain the weights of the BP neural network based on the particle swarm optimization algorithm. The specific steps are as follows:

Step 4.1. In the PSO algorithm, the particle dimension is set as two-dimensional, that is, the position of the i-th particle is set as x _i =(x _i1 , x _i2 ), where x _i1 and x _i2 represent the i-th particle respectively. The position component of the particle in two-dimensional space; set the corresponding particle velocity as v _i = (v _i1 , v _i2 ), where v _i1 and v _i2 respectively represent the velocity component of the i-th particle in two-dimensional space; the i-th The optimal historical position searched by a particle is p _i =(p _i1 , p _i2 ), that is, the individual extremum, where p _i1 and p _i2 respectively represent the optimal historical position components of the i-th particle in two-dimensional space; Set the optimal position searched by the entire particle swarm as p _g = (p _g1 , p _g2 ), that is, the global extremum, where p _g1 and p _g2 represent the optimal position obtained by the entire particle swarm in two-dimensional space weight;

Step 4.2, update the position and velocity of the particles, and use the following formula to update:

v _im (k+1)＝v _im (k)+c ₁ r ₁ (p _im (k)-x _im (k))+c ₂ r ₂ (p _gm (k)-x _im (k))

x _im (k+1)＝x _im (k)+v _im (k+1)

Among them, m=1, 2; x _im (k) represents the position of the i-th particle in the m-th dimension in the k-th iteration; v _im (k) represents the m-th dimension of the i-th particle in the k-th iteration dimensional speed; c ₁ is the weight coefficient of the particle tracking its own optimal position p _i =(p _i1 , p _i2 ), c ₂ is the weight of the particle tracking the optimal position of the particle swarm population p _g =(p _g1 , p _g2 ) coefficient; r ₁ and r ₂ are random numbers;

Step 4.3, calculate the fitness value of each particle according to the following formula,

Δe _i =y _out (i)-r _in (i)

According to the formula in step 4.2, update the individual extremum p _i =(p _i1 , p _i2 ) by taking the extreme value of the particle with the smallest fitness value, and update the global extremum p _g =( p _g1 , p _g2 ), the current global extremum p _g = (p _g1 , p _g2 ) is the optimal weight of the BP neural network found by the entire particle swarm;

Step 4.4, define the setting error function as: Determine whether the global extremum is smaller than the set error e _k , if yes, execute step 5, if not, execute step 4.5;

Step 4.5, determine whether the number of iterations is equal to the maximum number of iterations, if so, execute step 5, otherwise, add 1 to the number of iterations, and return to step 4;

Step 5. Output the global optimal weight p _g , and send the global optimal weight p _g to the BP neural network, and output the optimal PID control parameters;

Step 6, after the control unit processes the optimal PID control parameters output in step 5, it is transformed into a signal for controlling the opening of the fourth solenoid valve;

Step 7. Determine whether the deviation e(k) between the actual temperature and the set stable temperature is positive or negative. If e(k)<0, that is, r _in (k) is greater than y _out (k), then the control unit obtains through step 6 The signal controls the valve opening of the fourth solenoid valve to decrease; if e(k)>0, that is, r _in (k) is less than y _out (k), the control unit controls the valve of the fourth solenoid valve through the signal obtained in step 6 The opening increases.

Further, in step 1, it is set that the BP neural network adopts a three-layer structure, and the three-layer structure includes an input layer, a hidden layer and an output layer, wherein the hidden layer is set to 4 neurons, and the output layer is set to 3 neurons respectively represent the three control parameters K _p , K _i , K _d of the PID controller.

It can be seen from the above technical solution that the beneficial effect of the present invention lies in: a system and method for combined heating of an electric thermal storage boiler and solar energy provided by the present invention, the system can maximize the utilization of solar energy resources and combine the functions of the electric thermal storage boiler The electric thermal storage during the low-power period plays a role in peak-shaving and valley-filling, increasing the load rate of the power grid, ensuring the quality of heating, benefiting users and the power sector at the same time, and making heating less susceptible to environmental influences, effectively reducing the power consumption of heating. No emissions are generated in the system, and the stable heating of renewable energy is realized, which has a positive effect on promoting electric energy substitution technology, energy saving and emission reduction; the method described is a control method based on PSO-BP neural network, which combines BP neural network with PID Control, and use the particle swarm algorithm to optimize the parameters of the BP neural network, make full use of the advantages of the neural network's self-adaptation and self-learning ability, so that the BP neural network can converge quickly, and the PID room temperature control has strong time lag and low overshoot. , can effectively improve the accuracy and stability of the entire heating system.

Description of drawings:

Fig. 1 is a system structural block diagram provided by the embodiment of the present invention;

Fig. 2 is the circuit connection diagram of the central controller and its peripheral control circuit provided by the embodiment of the present invention;

FIG. 3 is a schematic structural diagram of a system provided by an embodiment of the present invention;

Fig. 4 is a schematic structural diagram of an algorithm in a method provided by an embodiment of the present invention;

Fig. 5 is the schematic diagram of BP neural network structure in the method that the embodiment of the present invention provides;

FIG. 6 is a flowchart of a method provided by an embodiment of the present invention.

In the figure: 1. Solar heat collector; 2. Heat exchanger; 3. First temperature sensor; 4. First circulating water pump; 5. First solenoid valve; 6. Electric heat storage boiler; 7. Second temperature sensor ; 8, the second circulating water pump; 9, the second solenoid valve; 10, the grid switching device; 11, the hot water tank; 1101, the insulation layer; 1102, the liquid level sensor; 13. The third solenoid valve; 14. The fourth circulating water pump; 15. The fourth solenoid valve; 16. Circulating pipeline; 17. Floor heating coil; 18. Water collector; 19. Water separator; 20. System control unit; 201, indoor temperature sensor; 21, water inlet pipe; 22, minimum liquid level.

detailed description:

The specific implementation manners of the present invention will be further described in detail below in conjunction with the accompanying drawings and embodiments. The following examples are used to illustrate the present invention, but are not intended to limit the scope of the present invention.

A combined heating system of an electric heat storage boiler and solar energy, as shown in FIG. The solar heat collection unit and the electric boiler unit are used to provide heating source for the system, which are the energy supply part of the system, and are connected to the heat storage unit; the heat storage unit is used to store hot water, which is the energy storage part of the system, and connected to the user pipe network unit ; The user pipe network unit is used as the channel of the system circulation and the heating terminal; the system control unit 20 is used to control other units to ensure the automatic operation of the system.

The solar heat collection unit includes a solar heat collector 1 , a heat exchanger 2 , a first temperature sensor 3 , a first circulating water pump 4 and a first solenoid valve 5 . The solar heat collector 1 is used for continuously heating the water therein under sunlight irradiation, and the water inlet of the solar heat collector 1 is connected to the water inlet pipe 21; the heat exchanger 2 is used for heat exchange with the solar heat collector 1; the first The temperature sensor 3 is located in the water outlet of the solar heat collector 1, and is used to detect the water temperature at the water outlet of the solar heat collector 1; the first circulating pump 4 and the first solenoid valve 5 are connected to the water outlet of the solar heat collector 1 The end is used to open the water outlet of the solar thermal collector 1, and extract the hot water in the solar thermal collector 1 to the heat exchanger 2 through the user pipe network unit.

The electric boiler unit includes an electric heat storage boiler 6 , a second temperature sensor 7 , a second circulating water pump 8 , a second solenoid valve 9 and a power grid switching device 10 . The electric thermal storage boiler 6 is used for operation during the low valley power consumption period at night under normal conditions and non-valley power consumption period during extreme weather, heating the water therein, and the water inlet of the electric thermal storage boiler 6 is connected to the water inlet pipe 21; The second temperature sensor 7 is located in the water outlet of the electric heat storage boiler 6 to detect the water temperature at the water outlet of the electric heat storage boiler 6; the second circulating water pump 8 and the second electromagnetic valve 9 are connected to the outlet of the electric heat storage boiler 6 The end of the water port is used to open the water outlet of the electric heat storage boiler 6, and extract the hot water in the electric heat storage boiler 6 to the heat storage unit through the user pipe network unit; power supply for operation.

The heat storage unit includes a hot water storage tank 11 , a third circulating water pump 12 , a third electromagnetic valve 13 , a fourth circulating water pump 14 and a fourth electromagnetic valve 15 . The hot water storage tank 11 includes an insulating layer 1101, a liquid level sensor 1102 at the inner bottom and a third temperature sensor 1103 in the water outlet. The liquid level sensor 1102 is used to detect the water level inside the hot water storage tank 11, and the third temperature sensor 1103 is used for Detect the water temperature at the water outlet of the hot water storage tank 11; the third circulating water pump 12 and the third solenoid valve 13 are sequentially connected to the upper end of the water inlet of the hot water storage tank 11 for passing the hot water of the heat exchanger 2 through the user pipe network The unit is pumped to the water storage tank 11; the fourth circulating water pump 14 and the fourth solenoid valve 15 are sequentially connected to the end of the water outlet of the water storage tank 11, and are used to open the water outlet of the water storage tank 11 and turn the water storage tank The hot water at 11 is extracted to the heating end of the user for heating.

The user pipe network unit includes circulation pipes 16 , floor heating coils 17 , water collectors 18 and water distributors 19 . Circulation pipe 16 is made of copper pipe, which is used to connect the waterways between the units in the system; floor heating coil 17 is a coiled water pipe arranged in a circle, which is the end of heating, and is used to provide heating for users; The water inlet of the pipe 17 is connected to the heat storage unit through the circulation pipe 16; the water separator 19 is arranged at the water outlet of the floor heating coil 17, and is connected with the water inlet of the solar heat collector 1 and the electric heat storage boiler 6 through the circulation pipe 16 .

The system control unit 20 includes an indoor temperature sensor 201, a central controller and its peripheral control circuits. The indoor temperature sensor 201 is used to collect the temperature in the user's room. The central controller adopts a single-chip microcomputer to receive the signals of each temperature sensor and liquid level sensor. After analysis and processing, a control signal is sent out to control the opening of each solenoid valve through the peripheral control circuit. 1. The operation of each circulating water pump and the operation of the power grid switching device 10 . In this embodiment, the connection circuit diagram of the central controller and its peripheral control circuit is shown in Figure 2. The system control unit is the key to realize the stable operation of the whole system, and mainly realizes room temperature control, water level signal acquisition, temperature signal acquisition, and control algorithm realization In this embodiment, the AT89S51 single-chip microcomputer is selected as the central controller, and the peripheral control circuit includes a clock chip DS1302 and a crystal oscillator.

AT89S51 is a low-power, high-performance CMOS 8-bit microcontroller. DS1302 is a high-performance, low-power real-time clock chip that can provide timing pulses for seconds, minutes, hours, days, weeks, months and years. In Figure 2, the Vcc pin of the DS1302 is connected to a 5V DC power supply to ensure the operation of the clock chip; X1 and X2 are oscillator sources, and an external 38kHz crystal oscillator is connected; is the reset/chip select line, by putting The input drive is set to high level to start all data transmission, and it is connected to the P2.0 pin of the microcontroller AT89S51; I/O is a serial data input and output terminal (bidirectional), connected to the P2.1 pin of the microcontroller AT89S51; SCLK It is the clock input terminal, connected to the P2.2 pin of the single-chip microcomputer AT89S51, through the single-chip microcomputer AT89S51 to set the low-peak power consumption time period (22:00-5:00 the next day), and control the grid to supply power to the electric boiler.

When the sunlight is insufficient, the calorific value cannot meet the requirements, and the liquid level in the heat storage tank 11 drops below the set minimum liquid level 22. At this time, the liquid level sensor 1102 sends a signal to the single-chip microcomputer AT89S51, and the P0.1 pin of the single-chip microcomputer AT89S51 is connected to the drive circuit, and then controls the connection to the power grid switching device 10, and starts the power grid switching device 10 to connect to the power grid Power supply, so as to achieve the purpose of controlling the grid to supply power to the electric heat storage boiler 6 when the sun is insufficient or continuous rainy weather, to ensure the continuous stability of heating. The first temperature sensor 3 in the water outlet of the solar collector 1, the second temperature sensor 7 in the water outlet of the electric heat storage boiler 6, the third temperature sensor 1103 in the water outlet of the hot water storage tank 11, and the indoor temperature sensor 201 are respectively connected with The P1.1 pin, P1.2 pin, P1.3 pin, and P1.4 pin of the single-chip microcomputer AT89S51 are connected, and the single-chip microcomputer AT89S51 collects the temperature of each temperature sensor through the P1.1-P1.4 pin. The P0.2 pin, P0.3 pin, P0.4 pin, and P0.5 pin of the single-chip microcomputer AT89S51 are respectively connected to the first circulating water pump 4 and the first solenoid valve 5, the second circulating water pump 8 and the second solenoid valve 9. The third circulating water pump 12, the third solenoid valve 13, the fourth circulating water pump 14 and the fourth solenoid valve 15, after receiving the signals sent by each sensor, the single chip microcomputer AT89S51 controls each Operation of solenoid valve and circulating water pump.

The schematic diagram of the combined heating system of the electric thermal storage boiler and solar energy provided in this embodiment is shown in Figure 3, and the dotted line in the figure is the control line. In the specific implementation, the system is operated in the following manner:

The system is replenished with liquid water from the water inlet 21. During the day, when the sunlight intensity is good, the unheated low-temperature water is pumped to the solar collector 1 through the water inlet 21. Under the sunlight, the solar collector 1. Absorb solar energy in real time and continuously heat the water in it. When the first temperature sensor 3 at the water outlet of the solar collector 1 detects that the outlet water temperature reaches a preset temperature value of 70°C, the system control unit 20 will control and start the first circulation pump 4, And control the opening of the first solenoid valve 5 to extract hot water to the heat exchanger 2 . When the liquid level sensor 1102 in the hot water storage tank 11 detects that the water is insufficient, the system control unit 20 controls to start the third circulating water pump 12, and controls the third electromagnetic valve 13 to open, so that the hot water in the heat exchanger 2 is passed through the user pipe. The grid unit is pumped to the hot water storage tank 11 for storage of hot water;

At night, the solar collector 1 cannot work. At this time, the electric heat storage boiler 6 is set to work from 22:00 to 5:00 the next day during the low-peak power consumption period, and the power grid switching device on the power supply side of the electric heat storage boiler 6 10. It can be controlled to connect to the power grid from 22:00 to 5:00 the next day to supply power to the electric heat storage boiler 6, and the electric boiler unit operates normally. The second temperature sensor 7 installed at the outlet of the electric heat storage boiler 6 detects that the electric boiler When the internal water temperature reaches the preset 70°C, the system control unit 20 controls to start the second circulating water pump 8, opens the second solenoid valve 9, and pumps hot water into the hot water storage tank 11;

When encountering extreme weather, such as when the intensity of sunlight is insufficient or the weather is cloudy and rainy for many consecutive days, the solar heat collection unit cannot operate normally, and the heat stored by the electric heat storage boiler 6 at night may not be sufficient for the user, causing the heat storage unit to The heat stored in the heat storage tank does not meet the requirements. At this time, the liquid level sensor 1102 located at the bottom of the hot water storage tank 11 can detect in real time that the water storage level in the hot water storage tank 11 is lower than the preset value. 1102 transmits the signal to the system control unit 20, and the system control unit 20 controls the grid switching device 10 to connect to the grid power supply, and starts the operation of the electric thermal storage boiler 6, even if it is not within the time range of 22:00-5:00 the next day, it needs Electric boiler 3 provides heat. In order to ensure the maximum use of solar heat, the minimum liquid level in the heat storage tank 6 is set to 22. Under normal operation, the liquid level in the heat storage tank 6 is above the minimum liquid level. When the heat storage is insufficient, That is, when the liquid level sensor 1102 detects that the liquid level is lower than the minimum liquid level 22, the system control unit 20 starts the power grid switching device 10, and uses the electric energy of the power grid to supply the electric heat storage boiler 6 to ensure continuous and stable operation of the system;

An indoor temperature sensor 201 is installed in the user's room, which can collect the indoor temperature in real time and upload it to the system control unit 20, and set the stable value of the room temperature at 20°C. When the indoor temperature sensor 201 detects that the room temperature is lower than 20°C, the system control unit 20 controls the fourth circulating water pump 14 to accelerate the cycle, and the opening of the fourth solenoid valve 15 is increased, so that the water storage tank 11 collects water flow through the water collector 7 The flow of water entering the floor heating coil 17 increases, thereby increasing the temperature of the room; when the indoor temperature sensor 201 detects that the room temperature is higher than 20°C, the system control unit 20 controls the fourth circulating water pump 14 to decelerate and cycle, and the fourth solenoid valve 15 The opening degree decreases, so that the water flow of the hot water storage tank 11 through the water collector 7 and into the floor heating coil 17 decreases, thereby reducing the room temperature. During this process, the diversion water flow of the water separator 19 increases simultaneously. or decrease. The low-temperature water flowing out from the heating end passes through the water separator 19, then flows through the circulation pipe 16 to supply the electric heat storage boiler 6 or the solar heat collector 1, and participates in recycling again to achieve the purpose of recycling.

The combined heating method of electric thermal storage boiler and solar energy provided by this embodiment is the control method of the above-mentioned system, which can adjust the parameters of the PID controller according to the operating state of the system, so as to achieve the optimization of performance indicators. The structure of the algorithm in the method is shown in Figure 4. The indoor temperature y _out (t) collected by the indoor temperature sensor, the set temperature r _in (t) and the difference e(t) between the two are used as the input layer of the BP neural network, and the output layer is the PID controller. The three control parameters K _p , K _i , K _d , introduce the PSO algorithm in the weight calculation of the BP neural network to solve the optimal weight value, and then output the optimal control parameters K _p , K _i , K _d , through the PID The controller further controls the opening degree of the fourth electromagnetic valve to keep the indoor temperature stable.

The PSO-BP neural network control algorithm used in PID temperature control, the BP neural network model used is shown in Figure 5, according to the relevant requirements of indoor temperature control, the number of nodes in the input layer, hidden layer, and output layer are selected respectively 3, 4, 3, the input is from the input layer through the hidden layer to the output layer, the state of neurons in each layer only affects the state of neurons in the next layer, and the weights of neurons in each layer are optimized by PSO algorithm.

The conventional incremental PID control algorithm is expressed as follows:

Δu(t)=u(t)-u(t-1)=K _p (e(t)-e(t-1))+K _i e(t)+K _d (e(t)-2e( t-1)+e(t-2))

In the formula: e(t), e(t-1) and e(t-2) represent the signal deviation of the tth, t-1 and t-2 samples respectively; u(t) is the output signal, Δu (t) represents the output signal deviation; K _p is a proportional parameter, K _i is an integral parameter, and K _d is a differential parameter.

The flow of the control method in this embodiment is shown in FIG. 6 , and specifically includes the following steps.

Step 1. Initialize various settings of the system, including setting the temperature value of each temperature sensor, setting the pressure value of the liquid level sensor, setting the temperature stability value in the user's room, initializing the topology of the BP neural network and initializing the particle swarm parameters. Initialize the topological structure of the BP neural network, and set the BP neural network to adopt a three-layer structure, in which the input layer neurons are set to 3, representing input, output and error respectively; the hidden layer is set to 4 neurons; the output layer It is set as 3 neurons, representing the three control parameters K _p , K _i , K _d of the PID controller respectively. In this embodiment, the temperature value of the end user is set to be stable at 20°C, the particle swarm parameters are initialized, the number of particles is 20, the weight coefficient c ₁ is 2, the weight coefficient c ₂ is 2, and the random numbers r ₁ and r ₂ are [ 0, 1] between uniformly distributed random numbers, the maximum number of iterations is 60.

Step 2. Collect the indoor temperature in real time, obtain the actual temperature r _in (k) of the indoor temperature sensor, the stable value of the indoor temperature y _out (k) set by the user, and the deviation e(k) between the actual temperature and the set stable temperature = y _out (k) - r _in (k), where y _out (k) = 20 °C.

Step 3. Calculate PID parameters, input r _in (k), y _out (k), e(k) as input signals into the input layer of the BP neural network, and calculate the output values K _p , K _i , K _d of the output layer.

Step 4. Obtain the weights of the BP neural network based on the PSO algorithm. The specific steps are as follows:

Step 4.1. In the PSO algorithm, the particle dimension is set as two-dimensional, that is, the position of the i-th particle is set as x _i =(x _i1 , x _i2 ), where x _i1 and x _i2 represent the i-th particle respectively. The position component of the particle in two-dimensional space; set the corresponding particle velocity as v _i = (v _i1 , v _i2 ), where v _i1 and v _i2 respectively represent the velocity component of the i-th particle in two-dimensional space; the i-th The optimal historical position searched by a particle is p _i =(p _i1 , p _i2 ), that is, the individual extremum, where p _i1 and p _i2 respectively represent the optimal historical position components of the i-th particle in two-dimensional space; Set the optimal position searched by the entire particle swarm as p _g = (p _g1 , p _g2 ), that is, the global extremum, where p _g1 and p _g2 represent the optimal position obtained by the entire particle swarm in two-dimensional space Component; the particle dimension indicates that the particle is looking for the optimal solution in a two-dimensional space, because the weights to be solved in this embodiment include two weights of the input layer and the hidden layer, so the dimension is set to two-dimensional , if there are three weights that need to be solved in the specific implementation, set the particle dimension to three-dimensional;

Step 4.2, update the position and velocity of the particles, and use the following formula to update:

v _im (k+1)＝v _im (k)+c ₁ r ₁ (p _im (k)-x _im (k))+c ₂ r ₂ (p _gm (k)-x _im (k))

x _im (k+1)＝x _im (k)+v _im (k+1)

Among them, m=1, 2; x _im (k) represents the position of the i-th particle in the m-th dimension in the k-th iteration; v _im (k) represents the m-th dimension of the i-th particle in the k-th iteration dimensional speed; c ₁ is the weight coefficient of the particle tracking its own optimal position p _i =(p _i1 , p _i2 ), c ₂ is the weight of the particle tracking the optimal position of the particle swarm population p _g =(p _g1 , p _g2 ) coefficient; r ₁ and r ₂ are random numbers;

Step 4.3, calculate the fitness value of each particle according to the following formula,

Δe _i =y _out (i)-r _in (i)

According to the formula in step 4.2, update the individual extremum p _i =(p _i1 , p _i2 ) by taking the extreme value of the particle with the smallest fitness value, and update the global extremum p _g =( p _g1 , p _g2 ), the current global extremum p _g = (p _g1 , p _g2 ) is the optimal weight of the BP neural network found by the entire particle swarm; update the position and speed according to the two formulas in step 4.2 , there are many particles in each generation, and each iteration can find out the optimal extremum of each particle and the entire particle swarm;

Step 4.4, define the setting error function as: Determine whether the global extremum is smaller than the set error e _k , if yes, execute step 5, if not, execute step 4.5;

Step 4.5: Determine whether the number of iterations is equal to the maximum number of iterations 60, if so, execute step 5, otherwise, increase the number of iterations by 1, and return to step 4.

Step 5. Output the global optimal weight p _g , and send the global optimal weight p _g to the BP neural network, and output the optimal PID control parameters K _p , K _i , K _d .

Step 6: The control unit processes the optimal PID control parameters K _p , K _i , and K _d output in step 5, and converts them into signals for controlling the opening of the fourth solenoid valve.

Step 7. Determine whether the deviation e(k) is positive or negative, that is, compare the actual temperature r _in (k) of the indoor temperature sensor with the set user indoor temperature stability value y _out (k), if e(k)<0, That is, r _in (k) is greater than y _out (k), and the actual indoor temperature is 20°C higher than the set temperature stability value, the control unit controls the valve opening of the fourth solenoid valve to decrease through the signal obtained in step 6; if e (k)>0, that is, r _in (k) is less than y _out (k), and the actual indoor temperature is lower than the set temperature stability value of 20°C, then the control unit controls the valve opening of the fourth solenoid valve through the signal obtained in step 6 degree increases.

The present invention provides a combined heating system and method of an electric heat storage boiler and solar energy. The system can maximize the use of solar energy resources, and combined with the electric heat storage during the low power period of the electric heat storage boiler, plays a role in peak shifting and filling. Valley, increase the load rate of the power grid, ensure the quality of heating, benefit users and the power sector at the same time, and make the heating less susceptible to environmental impacts, effectively reduce the power consumption of heating, without any emissions during the work process, and achieve the stability of renewable energy Heating, which plays a positive role in promoting electric energy substitution technology, energy saving and emission reduction, is a typical energy-saving green environmental protection product; the method described is a control method based on PSO-BP neural network, combining BP neural network with PID control, and using The particle swarm optimization algorithm optimizes the parameters of the BP neural network, making full use of the advantages of the neural network's self-adaptation and self-learning ability, so that the BP neural network can converge quickly, and the PID room temperature control has the characteristics of strong time lag and low overshoot, which can effectively improve Accuracy and stability of the entire heating system.

Finally, it should be noted that: the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that: it can still be Modifications are made to the technical solutions described in the foregoing embodiments, or equivalent replacements are made to some or all of the technical features; these modifications or replacements do not make the essence of the corresponding technical solutions depart from the scope defined by the claims of the present invention.

Claims (4)

1. a kind of electricity heat storage boiler and the system of solar association heating are it is characterised in that this system includes solar energy heating list Unit, thermal storage unit, electric boiler unit, user's pipe network unit and system control unit (20)；Described solar energy heating unit and electricity Boiler unit is used for providing warm source for system, is system energy supply section, is all connected with described thermal storage unit；Described accumulation of heat list Unit is used for storing hot water, is system energy storage part, is connected with described user's pipe network unit；Described user's pipe network unit conduct The passage of system circulation and heating end；System control unit (20) is used for controlling each unit it is ensured that the automatic running of system；

Described solar energy heating unit, including solar thermal collector (1), heat exchanger (2), the first temperature sensor (3), first follows Ring water pump (4) and the first electromagnetic valve (5)；It is therein that described solar thermal collector (1) is used under sunlight constantly heating Water, the water inlet of solar thermal collector (1) connects cold water pipe；Described heat exchanger (2) is used for carrying out heat with solar thermal collector (1) Exchange；Described first temperature sensor (3) in the outlet of solar thermal collector (1), for detecting solar thermal collector (1) water temperature of water outlet；Described first circulation water pump (4) and the first electromagnetic valve (5) are sequentially connected to described solar energy heating The outlet end of device (1), is used for opening the outlet of solar thermal collector (1), and the hot water in solar thermal collector (1) is led to Cross user's pipe network unit to extract to heat exchanger (2)；

Described electric boiler unit, including electric heat storage boiler (6), second temperature sensor (7), second circulation water pump (8), the second electricity Magnet valve (9) and electrical network switching device (10)；Described electricity heat storage boiler (6) is used for night dip electricity consumption period fortune under normal circumstances Non- valley power consumption period when row and extreme climate weather is run, and heats water therein, and the water inlet of electric heat storage boiler (6) connects Cold water pipe；Described second temperature sensor (7) in the outlet of electric heat storage boiler (6), for detecting electric heat storage boiler (6) The water temperature of water outlet；Described second circulation water pump (8) and the second electromagnetic valve (9) are sequentially connected to described electricity heat storage boiler (6) Outlet end, be used for opening the outlet of electric heat storage boiler (6), the hot water in electric heat storage boiler (6) managed by user Net unit extracts to thermal storage unit；Described electrical network switching device (10) connects electric network source, and the operation for electric heat storage boiler (6) supplies Electricity；

Described thermal storage unit, comprises hot water storage tank (11), the 3rd water circulating pump (12), the 3rd electromagnetic valve (13), the 4th recirculated water Pump (14) and the 4th electromagnetic valve (15)；Described hot water storage tank (11) includes heat-insulation layer (1101), the liquid level sensor of inner bottom part (1102) three-temperature sensor (1103) and in outlet, described liquid level sensor (1102) is used for detecting hot water storage tank (11) internal water level, described three-temperature sensor (1103) is used for detecting the water temperature of hot water storage tank (11) water outlet；Institute State the 3rd water circulating pump (12) and the 3rd electromagnetic valve (13) is sequentially connected to the water inlet upper end of hot water storage tank (11), for by institute The hot water stating heat exchanger (2) is extracted to thermal storage unit by user's pipe network unit；Described 4th water circulating pump (14) and the 4th electricity Magnet valve (15) is sequentially connected to the outlet end of hot water storage tank (11), is used for opening the outlet of hot water storage tank (11), will store The hot water extraction of boiler (11), to user's heating end, is heated；

Described user's pipe network unit, including circulating line (16), grounding heat coil tube (17), water collector (18) and water knockout drum (19)；Institute State circulating line (16) for the connection in water route between each unit in system；Described grounding heat coil tube (17) is heating end, is used for For user's heating；The water inlet located at described grounding heat coil tube (17) for the described water collector (18), by circulating line (16) and accumulation of heat Unit connects；The outlet located at described grounding heat coil tube (17) for the described water knockout drum (19), by circulating line (16) with described too The water inlet of sun energy heat collector (1) and electric heat storage boiler (6) connects；

Described system control unit (20), including indoor temperature transmitter (201), central controller and its peripheral control circuits, Indoor temperature transmitter (201) is used for gathering the indoor temperature of user, and central controller is used for receiving each temperature sensor and liquid The signal of level sensor (1102), sends control signal after processing by analysis, controls each electromagnetic valve by peripheral control circuits Aperture, the operation of each water circulating pump and electrical network switching device (10) operation.

2. a kind of system of electricity according to claim 1 heat storage boiler and solar association heating is it is characterised in that described Circulating line (16) adopts copper pipe；Grounding heat coil tube (17) is using the disk folding water pipe of convolution setting；Central controller is single-chip microcomputer.

3. a kind of electricity heat storage boiler and the method for solar association heating are it is characterised in that adopt the one kind described in claim 1 Electric heat storage boiler and the system realization of solar association heating, comprise the following steps that：

The every setting of step 1, initialization system, including the pressure of the temperature value of each temperature sensor of setting, setting liquid level sensor Force value, the temperature stabilization value setting user interior, the topological structure of initialization BP neural network and initialization particle swarm parameter；If Determine BP neural network and adopt three-decker, wherein input layer is set as 3 neurons, represent input, output and error respectively；

Step 2, indoor temperature is carried out with Real-time Collection, obtain actual temperature r of indoor temperature transmitter (201)_in(k), user Indoor temperature stationary value y setting_out(k) and actual temperature and deviation e (k) setting equilibrium temperature value, e (k)=y_out (k)-r_in(k)；

Step 3, by r_in(k)、y_outK (), e (k) input the input layer of BP neural network as input signal, calculate output layer Pid control parameter；

Step 4, based on particle cluster algorithm obtain BP neural network weights, comprise the following steps that：

In step 4.1, PSO algorithm, particle dimension is set as two dimension, that is,：Set the positional representation of i-th particle as x_i=(x_i1, x_i2), wherein, x_i1And x_i2Represent i-th particle location components in two-dimensional space respectively；Set corresponding particle rapidity to represent For v_i=(v_i1, v_i2), wherein v_i1And v_i2Represent i-th particle velocity component in two-dimensional space respectively；I-th particle search The optimum historical position arriving is p_i=(p_i1, p_i2), i.e. individual extreme value, wherein, p_i1And p_i2Represent i-th particle in two dimension respectively The optimum historical position component in space；Set the optimal location that whole population searches as p_g=(p_g1, p_g2), i.e. overall pole Value, wherein, p_g1And p_g2Represent the optimal location component in two-dimensional space that whole population obtains respectively；

The position of step 4.2, more new particle and speed, are updated using equation below：

v_im(k+1)=v_im(k)+c₁r₁(p_im(k)-x_im(k))+c₂r₂(p_gm(k)-x_im(k))

x_im(k+1)=x_im(k)+v_im(k+1)

Wherein, m=1,2；x_imThe position of m dimension in kth time iteration for i-th particle of (k) expression；v_imK () represents i-th The speed of m dimension in kth time iteration for the son；c₁For Particle tracking oneself optimal location p_i=(p_i1, p_i2) weight coefficient, c₂ For Particle tracking population colony optimal location p_g=(p_g1, p_g2) weight coefficient；r₁、r₂For random number；

Step 4.3, calculate the fitness value of each particle according to the following formula,

Δe_i=y_out(i)-r_in(i)

According to formula in step 4.2, take fitness value little particle extreme value more new individual extreme value p_i=(p_i1, p_i2), round a grain In subgroup, the minimum extreme value of fitness value updates global extremum p_g=(p_g1, p_g2), current global extremum p_g=(p_g1, p_g2) be The BP neural network best initial weights that currently whole population searches out；

Step 4.4, definition set error function as：Judge global extremum whether less than setting error e_k, if so, then execution step 5, if it is not, then execution step 4.5；

Step 4.5, judge that whether iterationses are equal to maximum iteration time, if so, then execution step 5, otherwise, then iterationses Plus 1, and return to step 4；

Step 5, output global optimum weights p_g, and by global optimum weights p_gSend into BP neural network, export optimum PID control Parameter；

The optimum PID control parameter that step 6, control unit export to step 5 after treatment, is changed into control the 4th electromagnetic valve (15) signal of aperture；

Step 7, judge actual temperature with set equilibrium temperature value deviation e (k) positive and negative, if e (k) ＜ 0, i.e. r_inK () is more than y_outK (), then the valve opening of signal control the 4th electromagnetic valve (15) that control unit is obtained by step 6 reduces；If e (k) is ＞ 0, i.e. r_inK () is less than y_out(k), then the valve opening of signal control the 4th electromagnetic valve (15) that control unit is obtained by step 6 Increase.

4. a kind of method of electricity according to claim 3 heat storage boiler and solar association heating is it is characterised in that described Set BP neural network in step 1 and adopt three-decker, described three-decker includes input layer, hidden layer and output layer, wherein Hidden layer is set as 4 neurons, and output layer is set as 3 neurons；3 neurons of output layer represent PID control respectively Three control parameters K of device_p、K_i、K_d.