阅读说明：本技术 一种地源热泵自适应智能模糊控制系统及方法 (Ground source heat pump self-adaptive intelligent fuzzy control system and method ) 是由 刘海峰 雷江平 周佩祥 郑松松 陈士俊 刘黎明 项镭 潘康 柏卫平 王春 杜宇航 于 2021-09-16 设计创作，主要内容包括：本发明主要是针对地源热泵单一控制手段高非线性、强时变性以及难消除稳态误差等控制难点,提供了一种灵活、可靠、稳定的地源热泵自适应智能模糊控制系统,包括第一加法器、微分器、偏差放大器、模糊控制器、PID控制器、末端风机子系统、Smith预估器、第二加法器和第三加法器,在自适应模糊控制规则下,整合研究自适应控制及模糊控制技术,使得在多扰动的实际运行状况下,系统的控制精度与响应速度均达到电能替代领域要求,改进地源热泵系统的控制性能,提高地源热泵系统“以电代煤”的效率,促进其在电能替代领域的推广应用。(The invention mainly aims at the control difficulties of high nonlinearity, strong time-varying property, difficult elimination of steady-state error and the like of a single control means of a ground source heat pump, and provides a flexible, reliable and stable ground source heat pump self-adaptive intelligent fuzzy control system which comprises a first adder, a differentiator, a deviation amplifier, a fuzzy controller, a PID (proportion integration differentiation) controller, a terminal fan subsystem, a Smith predictor, a second adder and a third adder.)

1. The ground source heat pump self-adaptive intelligent fuzzy control system is characterized by comprising a first adder, a differentiator, a deviation amplifier, a fuzzy controller, a PID (proportion integration differentiation) controller, a tail end fan subsystem, a Smith predictor, a second adder and a third adder, wherein a temperature difference set value is input at the input end of the first adder, the output end of the first adder is respectively connected with the input end of the differentiator, the input end of the deviation amplifier and the input end of the PID controller, and the output end of the differentiator is connected with the input end of the fuzzy controller after being connected with the deviation amplifier; the proportional output end, the integral output end and the differential output end of the fuzzy controller are respectively and correspondingly connected with the proportional input end, the integral input end and the differential input end of the PID controller, the output end of the PID controller is respectively connected with the input end of the terminal fan subsystem and the input end of the Smith predictor, and the output end of the Smith predictor and the output end of the terminal fan subsystem are both connected with the input end of the second adder; the input end of the third adder inputs the output of the second adder and the output of the Smith predictor without considering the pure time delay link of the terminal fan subsystem; the input end of the first adder is also input with the output of the third adder.

2. The ground source heat pump adaptive intelligent fuzzy control system of claim 1, wherein the terminal fan subsystem outputs a circulating working medium for returning a temperature difference Δ T.

3. The adaptive intelligent fuzzy control system of the ground source heat pump according to claim 2, wherein the Smith predictor comprises G_n(s) Module and e^-τsA module therein

4. The ground source heat pump self-adaptive intelligent fuzzy control method is characterized in that a combined algorithm is adopted, and the combined algorithm comprises a deviation gain link, a fuzzy control link, a PID control link and a Smith estimation link.

5. The ground source heat pump self-adaptive intelligent fuzzy control method according to claim 4, characterized in that the specific process of the deviation gain link is as follows: scaling the input to the fuzzy controller, (e, ec) K_(e,ec)(e₀,ec₀) In the formula e₀,ec₀And e, ec respectively represent the deviation amount of the temperature difference before and after amplification and the change rate thereof, K_(e,ec)Is the gain factor.

6. The ground source heat pump adaptive intelligent fuzzy control method according to claim 4 or 5, characterized in that the specific process of the fuzzy control link comprises the following steps:

A1) determining input/output variable domains, fuzzy language subsets and membership functions;

A2) the fuzzy controller proceeds under the guidance of fuzzy rulesLine reasoning, after the clarification process, the variable quantity delta K of the PID parameter is output_p、ΔK_i、ΔK_d。

7. The ground source heat pump self-adaptive intelligent fuzzy control method according to claim 6, characterized in that the PID control link comprises the following specific processes: and superposing the output of the fuzzy controller on a PID parameter preset value to obtain an optimal control parameter.

8. The ground source heat pump adaptive intelligent fuzzy control method according to claim 7, characterized in that the closed-loop transfer function is:

Technical Field

The invention relates to the technical field of ground source heat pumps, in particular to a self-adaptive intelligent fuzzy control system and method of a ground source heat pump.

Background

In the present society, the problems of air pollution and exhaustion of chemical energy are becoming more severe, and the promotion of energy structure transformation and the improvement of energy utilization efficiency become the focus of attention. The energy strategy should implement the policy of open source, i.e. receiving and using renewable energy as much as possible, and the main task of throttling is to save energy and improve the energy utilization efficiency. The shallow earth surface absorbs 47% of solar radiation energy, and low-grade heat energy stored in the shallow earth surface is called as 'geothermal energy', and the shallow earth surface is used as an unlimited renewable clean energy source, so that how to utilize the shallow earth surface is worthy of thinking. Under the background, an 'electric energy replacement strategy' is produced at the right moment, and a ground source heat pump is taken as a classic example of 'replacing coal with electricity' which is one of the core ideas of the strategy, so that the method is widely popularized by national power grid companies. The cold and heat of the ground source heat pump system are obtained from a rock-soil layer with a constant temperature working condition, the energy efficiency ratio (COP) value of a unit is kept above 4, namely, 1kW of high-grade electric energy is consumed, so that about 4kW of cold or heat can be obtained, and the operation efficiency is extremely high; under the refrigeration working condition, compared with a common central air-conditioning system, the ground source heat pump air-conditioning system can save energy by 30-40 percent; under the heating working condition, compared with a gas boiler floor heating system, the energy can be saved by 40-50%, and the energy conservation and emission reduction are remarkable.

On one hand, compared with the traditional air-conditioning system, the design of the ground source heat pump air-conditioning system hardware device adds a lot of contents, the conditions for realizing the air quantity and the water quantity of the whole system are more complex compared with the traditional air-conditioning system, and a new more intelligent control means is needed to be adopted in order to ensure the good control performance of the water quantity and the air quantity of the ground source heat pump air-conditioning system; on the other hand, the actual industrial control field conditions are more and more complex at present, the single control means of the ground source heat pump has the control difficulties of high nonlinearity, strong time-varying property, difficulty in eliminating steady-state errors and the like, and an accurate mathematical model is difficult to establish, so that the control precision and the response speed of the system are difficult to meet the requirements of the electric energy substitution field under the actual operation condition of multiple disturbances. In the prior art, researches on intelligent control methods include fuzzy control, genetic algorithm, neural network algorithm and the like.

Disclosure of Invention

The invention mainly aims at the control difficulties of high nonlinearity, strong time-varying property, difficult elimination of steady-state error and the like of a single control means of a ground source heat pump, provides a flexible, reliable and stable self-adaptive intelligent fuzzy control system and method of the ground source heat pump, integrates and researches self-adaptive control and fuzzy control technology under the self-adaptive fuzzy control rule, ensures that the control precision and the response speed of the system meet the requirements of the electric energy substitution field under the actual operation condition of multiple disturbances, improves the control performance of the ground source heat pump system, improves the efficiency of the ground source heat pump system for replacing coal with electricity, and promotes the popularization and application of the ground source heat pump system in the electric energy substitution field.

In order to achieve the purpose, the invention adopts the following technical scheme:

a self-adaptive intelligent fuzzy control system of a ground source heat pump comprises a first adder, a differentiator, a deviation amplifier, a fuzzy controller, a PID controller, a tail end fan subsystem, a Smith predictor, a second adder and a third adder, wherein a temperature difference set value is input at the input end of the first adder, the output end of the first adder is respectively connected with the input end of the differentiator, the input end of the deviation amplifier and the input end of the PID controller, and the output end of the differentiator is connected with the rear end of the deviation amplifierThe input end of the fuzzy controller is connected with the input end of the fuzzy controller; the proportional output end, the integral output end and the differential output end of the fuzzy controller are respectively and correspondingly connected with the proportional input end, the integral input end and the differential input end of the PID controller, the output end of the PID controller is respectively connected with the input end of the terminal fan subsystem and the input end of the Smith predictor, and the output end of the Smith predictor and the output end of the terminal fan subsystem are both connected with the input end of the second adder; the input end of the third adder inputs the output of the second adder and the output of the Smith predictor without considering the pure time delay link of the terminal fan subsystem; the input end of the first adder is also input with the output of the third adder. Because reasonable control over the compressor unit and the tail end fan is an important way for improving the performance of the ground source heat pump system, besides the self-adaptive intelligent fuzzy control system aiming at the tail end fan subsystem, the tail end fan subsystem can be replaced by the compressor unit subsystem, and the self-adaptive intelligent fuzzy control system aiming at the compressor unit subsystem is constructed, so that the control performance of the ground source heat pump system is further improved, the difficulty that the self-adaptive capacity and the energy-saving benefit of the traditional ground source heat pump control system are poor is broken, the efficiency of replacing coal with electricity of the ground source heat pump system is improved, and the popularization and application of the ground source heat pump system in the field of electric energy replacement are promoted. In the system, a first adder inputs a temperature difference set value and a negative value output by a third adder; before inputting the temperature difference deviation and the change rate thereof, the fuzzy controller performs proportional amplification through the deviation amplifier, and correspondingly outputs PID parameter change delta K through a proportional output end, an integral output end and a differential output end_p、ΔK_i、ΔK_d(ii) a PID parameter variation delta K input into fuzzy controller and output by PID controller_p、ΔK_i、ΔK_dAnd the temperature difference deviation value output by the first adder; the second adder inputs the circulating working medium output by the fan subsystem at the tail end for supplying the return temperature difference delta T and the negative value output by the Smith predictor; the third adder inputs the output of the pure time delay link of the tail end fan subsystem and the output of the second adder which are not included in the Smith predictor. The system integrates and researches adaptive control and fuzzy control technology under the adaptive fuzzy control rule, so thatUnder the actual operation condition of multiple disturbances, the control precision and the response speed of the system both meet the requirements of the electric energy substitution field, the control performance of the ground source heat pump system is improved, the efficiency of replacing coal with electricity of the ground source heat pump system is improved, and the popularization and the application of the system in the electric energy substitution field are promoted.

Preferably, the tail end fan subsystem outputs a circulating working medium for supplying the return temperature difference delta T. The output end of the tail end fan subsystem is connected with the input end of the second adder and also serves as the output end of the system, and the circulating working medium is output to supply the return temperature difference delta T.

Preferably, the Smith predictor comprises G_n(s) Module and e^-τsA module therein G_n(s) block indicates no pure delay part, e^-τsThe module represents a pure time delay part, and the introduction of a Smith predictor is equivalent to the forward movement of a system feedback point and the compensation of a pure time delay link of a terminal fan subsystemThe resulting loss of control performance, the introduced Smith predictor has the following form:

G_n(s)e^-τs

whereinAnd isThe closed loop transfer function of the whole system at this time is as follows:

a ground source heat pump self-adaptive intelligent fuzzy control method adopts the ground sourceThe heat pump self-adaptive intelligent fuzzy control system comprises a deviation gain link, a fuzzy control link, a PID control link and a Smith estimation link. The specific process is as follows: firstly, the deviation gain link amplifies the input of the fuzzy controller in proportion (e, ec) to K_(e,ec)(e₀,ec₀) In the formula e₀,ec₀And e, ec respectively represent the deviation amount of the temperature difference before and after amplification and the change rate thereof, K_(e,ec)The gain coefficient is used for improving the error identification precision; secondly, input/output variable discourse domain, fuzzy language subset and membership function are determined, the fuzzy controller carries out reasoning under the guidance of fuzzy rule, and variable quantity delta K of PID parameter is output after the clarification process_p、ΔK_i、ΔK_d(ii) a Then the PID control link outputs the delta K output by the fuzzy controller_p、ΔK_i、ΔK_dSuperimposed to the PID parameter preset value K_p0、K_i0、K_d0As the optimum control parameter K_p、K_i、K_d(ii) a And finally, introducing a Smith predictor, namely connecting a Smith prediction loop in parallel, equivalently moving a system feedback point forward to compensate a pure time delay link of a terminal fan subsystemThe resulting loss of control performance, the introduced Smith prediction loop has the following form: g_n(s)e^-τsWhereinAnd isThe closed loop transfer function of the whole system at this time is as follows:

the method is realized by a combined algorithm, and the control process of the combined algorithm combined with the variable flow rate temperature difference strategy is as follows: the fuzzy controller uses the deviation value and the change rate of the temperature difference between the two sides of the circulating working medium supply and return flowAs input, the clear input quantity is mapped to the input fuzzy set through the fuzzification process, the fuzzy inference engine takes the fuzzy rule as guidance to make a decision, and the PID parameter variation delta K is obtained through the clarification process_p、ΔK_i、ΔK_dAnd then the optimal control parameter is superposed on the preset value of the PID parameter to be used as the optimal control parameter, namely the optimal control parameter is as follows:

K_P＝K_P0+ΔK_P

K_I＝K_I0+ΔK_I

K_D＝K_D0+ΔK_D。

while the optimal control parameters regulate the control process, a pure time delay (lag) link of a control object is compensated through a parallel Smith pre-estimation loop. The combination algorithm combines the robustness of PID control and the flexibility of fuzzy control, compensates the performance loss caused by a time delay link through a Smith pre-estimation loop, and improves the self-adaptive capacity of the ground source heat pump control system.

Preferably, the specific process of the deviation gain element is as follows: scaling the input to the fuzzy controller, (e, ec) K_(e,ec)(e₀,ec₀) In the formula e₀,ec₀And e, ec respectively represent the deviation amount of the temperature difference before and after amplification and the change rate thereof, K_(e,ec)Is a gain factor to improve the error recognition accuracy.

Preferably, the specific process of the fuzzy control link includes the following steps: A1) determining input/output variable domains, fuzzy language subsets and membership functions; A2) the fuzzy controller carries out reasoning under the guidance of fuzzy rules, and outputs the variable quantity delta K of the PID parameters after the clarification process_p、ΔK_i、ΔK_d. Firstly, determining input/output variable discourse domain, fuzzy language subset and membership function, then making inference by fuzzy controller under the guidance of fuzzy rule, and after the process of clarification outputting variable quantity delta K of PID parameter_p、ΔK_i、ΔK_dTo obtain the optimal control parameters.

Preferably, the specific process of the PID control link is as follows:the output of the fuzzy controller is superposed on the PID parameter preset value as the optimal control parameter, namely the PID control link outputs the delta K of the fuzzy controller_p、ΔK_i、ΔK_dSuperimposed to the PID parameter preset value K_p0、K_i0、K_d0As the optimum control parameter K_p、K_i、K_dThe control process is then adjusted by the optimal control parameters.

Preferably, the closed loop transfer function is:

the Smith prediction loop introduced has the following form: g_n(s)e^-τsWhereinAnd isThe transformation transfer function of the whole system is:

wherein C(s) is a transfer function of the system,G_fan(s)＝G(s)e^-τsthus, the closed loop transfer function of the control system is as follows:

therefore, the invention has the advantages that:

(1) under the actual operation condition of multiple disturbances, the control precision and the response speed of the system both meet the requirements of the electric energy substitution field, the control performance of a ground source heat pump system is improved, the efficiency of replacing coal with electricity of the ground source heat pump system is improved, and the popularization and the application of the system in the electric energy substitution field are promoted;

(2) the control difficulties of high nonlinearity, strong time-varying property, difficult elimination of steady-state error and the like of a single control means of the ground source heat pump are overcome, and the method has the advantages of flexibility, reliability, stability and the like.

Drawings

Fig. 1 is a schematic structural diagram of an adaptive intelligent fuzzy control system of a ground source heat pump in the embodiment of the invention.

FIG. 2 is a fuzzy membership function image in an embodiment of the present invention.

1. The system comprises a first adder 2, a differentiator 3, a deviation amplifier 4, a fuzzy controller 5, a PID controller 6, an end fan subsystem 7, a Smith predictor 8, a second adder 9 and a third adder.

Detailed Description

The invention is further described with reference to the following detailed description and accompanying drawings.

As shown in fig. 1, a ground source heat pump adaptive intelligent fuzzy control system comprises a first adder 1, a differentiator 2, a deviation amplifier 3, a fuzzy controller 4, a PID controller 5, a terminal fan subsystem 6, a Smith predictor 7, a second adder 8 and a third adder 9, wherein a temperature difference set value is input at an input end of the first adder 1, an output end of the first adder 1 is respectively connected with an input end of the differentiator 2, an input end of the deviation amplifier 3 and an input end of the PID controller 5, and an output end of the differentiator 2 is connected with the deviation amplifier 3 and then is connected with an input end of the fuzzy controller 4; the proportional output end, the integral output end and the differential output end of the fuzzy controller 4 are correspondingly connected with the proportional input end, the integral input end and the differential input end of the PID controller 5 respectively, the output end of the PID controller 5 is connected with the input end of the tail-end fan subsystem 6 and the input end of the Smith predictor 7 respectively, and the output end of the Smith predictor 7 and the output end of the tail-end fan subsystem 6 are connected with the input end of the second adder 8; the input end of the third adder 9 inputs the output of the second adder 8 and the output of the Smith predictor 7 without considering the pure time delay link of the terminal fan subsystem 6; the input of the first adder 1 also inputs the output of the third adder 9. Due to the pair of compressor unitsAnd reasonable control of the tail end fan is an important way for improving the performance of the ground source heat pump system, and besides constructing a self-adaptive intelligent fuzzy control system aiming at the tail end fan subsystem 6, the tail end fan subsystem 6 can be replaced by a compressor unit subsystem to construct the self-adaptive intelligent fuzzy control system aiming at the compressor unit subsystem, so that the control performance of the ground source heat pump system is further improved. In the system, a first adder 1 inputs a temperature difference set value and a negative value output by a third adder 9; before inputting the temperature difference deviation and the change rate thereof, the fuzzy controller 4 firstly carries out proportional amplification through the deviation amplifier 3, and the fuzzy controller 4 correspondingly outputs PID parameter change delta K through a proportional output end, an integral output end and a differential output end_p、ΔK_i、ΔK_d(ii) a The PID controller 5 inputs the parameter variation delta K output by the fuzzy controller 4_p、ΔK_i、ΔK_dAnd the temperature difference deviation value output by the first adder 1; the second adder 8 inputs the circulating working medium output by the fan subsystem 6 at the tail end for supplying the return temperature difference delta T and the negative value output by the Smith predictor 7; the third adder 9 inputs the output of the Smith predictor 7 which does not include the pure time delay link of the end fan subsystem 6 and the output of the second adder 8.

As shown in fig. 1, the end fan subsystem 6 outputs the circulating working medium for returning the temperature difference Δ T. The output end of the tail end fan subsystem 6 is not only connected with the input end of the second adder 8, but also serves as the output end of the system, and the circulating working medium is output to supply the return temperature difference delta T.

As shown in FIG. 1, the Smith predictor 7 includes G_n(s) Module and e^-τsA module therein G_n(s) block indicates no pure delay part, e^-τsThe module represents a pure time delay part, and the introduction of the Smith predictor 7 is equivalent to the forward movement of a system feedback point and the compensation of a pure time delay link of the tail end fan subsystem 6The resulting loss of control performance, the introduced Smith predictor 7 has the form:

G_n(s)e^-τs

whereinAnd isThe closed loop transfer function of the whole system at this time is as follows:

a ground source heat pump self-adaptive intelligent fuzzy control method adopts the ground source heat pump self-adaptive intelligent fuzzy control system and comprises a deviation gain link, a fuzzy control link, a PID control link and a Smith estimation link. The specific process is as follows: firstly, the deviation gain element amplifies the input of the fuzzy controller 4 in proportion (e, ec) to K_(e，ec)(e₀，ec₀) In the formula e₀，ec₀And e, ec respectively represent the deviation amount of the temperature difference before and after amplification and the change rate thereof, K_(e，ec)Is the gain factor; secondly, input/output variable discourse domain, fuzzy language subset and membership function are determined, the fuzzy controller 4 conducts reasoning under the guidance of fuzzy rule, and variable quantity delta K of PID parameter is output after the clarification process_p、ΔK_i、ΔK_d(ii) a Then the PID control link outputs the delta K output by the fuzzy controller 4_p、ΔK_i、ΔK_dSuperimposed to the PID parameter preset value K_p0、K_i0、K_d0As the optimum control parameter K_p、K_i、K_d(ii) a And finally, introducing a Smith predictor 7, namely connecting a Smith prediction loop in parallel, equivalently moving a system feedback point forward to compensate a pure time delay link of the tail end fan subsystem 6The resulting loss of control performance, the introduced Smith prediction loop has the following form: g_n(s)e^-τsWhereinAnd isThe transformation transfer function of the whole system is:

wherein C(s) is a transfer function of the system,G_fan(s)＝G(s)e^-τsthus, the closed loop transfer function of the control system is as follows:

the method is realized by a combined algorithm, and the control process of the combined algorithm combined with the variable flow rate temperature difference strategy is as follows: the fuzzy controller 4 takes the deviation value and the change rate of the temperature difference of the two sides of the circulation working medium supply reflux as input, the clear input quantity is mapped to an input fuzzy set through the fuzzification process, the fuzzy inference engine takes the fuzzy rule as guidance to make a decision, and the PID parameter change quantity delta K is obtained through the clarification process_p、ΔK_i、ΔK_dAnd then the optimal control parameter is superposed on the preset value of the PID parameter to be used as the optimal control parameter, namely the optimal control parameter is as follows:

K_P＝K_P0+ΔK_P

K_I＝K_I0+ΔK_I

K_D＝K_D0+ΔK_D。

while the optimal control parameters regulate the control process, a pure time delay (lag) link of a control object is compensated through a parallel Smith pre-estimation loop. The combination algorithm combines the robustness of PID control and the flexibility of fuzzy control, compensates the performance loss caused by a time delay link through a Smith pre-estimation loop, and improves the self-adaptive capacity of the ground source heat pump control system.

Wherein, for the compressor unit subsystem and the end fan subsystem 6, a seven-level fuzzy language set Variable ═ { NB, NM, NS, ZO, PS, PM, PB }, the fuzzy controller 4 is in a two-input-three-output form, and the determination of the input/output Variable domain is shown in the following table 4-1:

TABLE 4-1 selection of input/output variable discourse fields

The fuzzy control membership functions all adopt a Trimf triangular form shown in FIG. 2;

the fuzzy rules are designed as shown in tables 4-2 to 4-4, wherein fuzzy language subsets are abbreviated as NB, NM, NS, ZO, PS, PM, PB, and respectively represent "negative large, negative medium, negative small, zero, positive small, positive medium, and positive large".

TABLE 4-2 Δ Kp fuzzy rule design

e\ec

NB

NM

NS

ZO

PS

PM

PB

NB

PB

PB

PM

PM

PS

ZO

ZO

NM

PB

PB

PM

PS

PS

ZO

NS

NS

PM

PM

PM

PS

ZO

NS

NS

ZO

PM

PM

PS

ZO

NS

NM

NM

PS

PS

PS

ZO

NS

NS

NM

NM

PM

PS

ZO

NS

NM

NM

NM

NB

PB

ZO

ZO

NM

NM

NM

NB

NB

Tables 4-3 Δ Ki fuzzy rule design

e\ec

NB

NM

NS

ZO

PS

PM

PB

NB

NB

NB

NM

NM

NS

ZO

ZO

NM

NB

NB

NM

NS

NS

ZO

ZO

NS

NM

NM

NS

NS

ZO

PS

PS

ZO

NM

NM

NS

ZO

PS

PM

PM

PS

NM

NS

ZO

PS

PS

PM

PB

PM

ZO

ZO

PS

PS

PM

PB

PB

PB

ZO

ZO

PS

PM

PM

PB

PB

Tables 4-4. delta. K_dFuzzy rule design of