阅读说明：本技术 一种锅炉汽包水位控制系统及方法 (Boiler drum water level control system and method ) 是由 兰扬 张娅 于 2021-09-30 设计创作，主要内容包括：本发明属于电力控制技术领域,具体涉及一种锅炉汽包水位控制系统及方法；该方法包括：步骤一、建立PI控制器及自适应模糊PID控制器；其中,PI控制器为比例主导控制器；自适应模糊PID控制器为常规PID控制的基础上,根据系统当前误差e和误差变化率ec,利用模糊推理实时整定P、I、D参数的控制器；步骤二,采集上一时刻水位的误差e,并判断误差e是否大于设定偏差；若大于则转到步骤三,若不大于则转到步骤四；步骤三,采用PI控制器控制锅炉给水流量阀门的开度,并返回步骤一,对下一时刻水位进行控制；步骤四,采用自适应模糊PID控制器控制锅炉给水流量阀门的开度,并返回步骤一。本申请能够精确且快速的控制锅炉汽包水位。(The invention belongs to the technical field of electric control, and particularly relates to a boiler drum water level control system and a boiler drum water level control method; the method comprises the following steps: step one, establishing a PI controller and a self-adaptive fuzzy PID controller; wherein, the PI controller is a proportion leading controller; the self-adaptive fuzzy PID controller is a controller for setting P, I, D parameters in real time by using fuzzy reasoning according to the current error e and the error change rate ec of the system on the basis of conventional PID control; step two, acquiring an error e of the water level at the previous moment, and judging whether the error e is greater than a set deviation; if the value is larger than the preset value, turning to the step three, and if the value is not larger than the preset value, turning to the step four; step three, controlling the opening of a boiler feed water flow valve by adopting a PI controller, returning to the step one, and controlling the water level at the next moment; and step four, adopting a self-adaptive fuzzy PID controller to control the opening of a boiler feed water flow valve, and returning to the step one. This application can accurate and quick control boiler drum water level.)

1. A boiler drum water level control method is characterized by comprising the following steps:

step one, establishing a PI controller and a self-adaptive fuzzy PID controller; wherein, the PI controller is a proportion leading controller; the self-adaptive fuzzy PID controller is a controller for setting P, I, D parameters in real time by using fuzzy reasoning according to an error e and an error change rate ec on the basis of conventional PID control;

step two, acquiring an error e of the water level at the previous moment, and judging whether the error e is greater than a set deviation; if the value is larger than the preset value, turning to the step three, and if the value is not larger than the preset value, turning to the step four;

step three, controlling the opening of a boiler feed water flow valve by adopting a PI controller, returning to the step one, and controlling the water level at the next moment;

and step four, adopting a self-adaptive fuzzy PID controller to control the opening of a boiler feed water flow valve, and returning to the step one to control the water level at the next moment.

2. The boiler drum water level control method according to claim 1, characterized in that: in the step one, in the built self-adaptive fuzzy PID controller, the input quantity comprises an error e and an error change rate ec, and the output quantity comprises a delta K_p、△K_iAnd Δ K_d(ii) a And obtaining an output quantity according to the input quantity and a preset fuzzy setting rule.

3. The boiler drum water level control method according to claim 2, characterized in that: in the first step, a variable domain factor alpha is introduced on the basis of an adaptive fuzzy PID controller, the actual error basic domain is [ -alpha (E) E, alpha (E) E ], the error change rate basic domain is [ -alpha (EC) EC, alpha (EC) EC ], and the output domain is [ -alpha-delta Kp, ], [ -alpha-delta Ki, alpha-delta Ki ], [ -alpha-delta Kd, -alpha-delta Kd ];

the input domain expansion factor is alpha (x) 1-lambdae^-kx·xWherein, λ ∈ (0,1), k>0; the output domain expansion factor is alpha (x) ═ (| x |/E) tau + epsilon, where tau>0, λ, k, τ, ε are constants.

4. The boiler drum water level control method according to claim 3, characterized in that: input error e, error change rate ec and output delta K of self-adaptive fuzzy PID controller_p、△K_i、△K_dThe language variable values are all negative big, negative middle, negative small, zero, positive small, middle and positive big, and the discourse domain is set as [ -6, 6]The quantization levels are { -6, -5, -4, -3, -2, -1, 0,1, 2, 3, 4, 5, 6} and the membership functions of the fuzzy subsets of all linguistic variables are triangular membership functions.

5. The boiler drum water level control method according to claim 4, characterized in that: the fuzzy setting rule of the parameters in the self-adaptive fuzzy PID controller is as follows:

according to the value range, respectively is Δ K_p、△K_iAnd Δ K_dSetting a large value area, a medium value area and a small value area;

when e is greater than the first threshold value, the delta K of the large value area is taken_pAnd Δ K_iIf e × ec<0, taking the Delta K of the median region_d(ii) a If e x ec>0, then taking the delta K of the large value area_d；

When e is greater than the second threshold value and smaller than the first threshold value, taking the delta K of the small value area_pAnd Δ K of median region_i(ii) a If e x ec<0, taking the Delta K of the large value area_d(ii) a If e x ec>0, then taking the delta K of the median region_d；

When e is less than or equal to the second threshold, taking the delta K of the median area_pAnd delta K of small value region_i(ii) a If e x ec<0, taking the Delta K of the small value area_d(ii) a If e x ec>0, taking the Delta K of the median region_d。

6. The boiler drum water level control method according to claim 5, characterized in that: in the fourth step, when the self-adaptive fuzzy PID model is adopted to control the opening degree of the boiler feed water flow valve, firstly, the PID setting method based on the optimization function is utilized to obtain an initial parameter K_p0、K_i0、K_d0And then the fuzzy reasoning is obtained through a self-adaptive fuzzy PID modelVariation Δ K to each parameter_p、△K_i、△K_dSuperposing the parameter variation and the initial parameter to obtain a real-time setting K_p、K_i、K_dAnd after the parameters are obtained, the output quantity of the controller is obtained, and the opening of the valve is controlled according to the output quantity of the controller.

7. The boiler drum water level control method according to claim 6, characterized in that: in the fourth step, an incomplete differential element is also included, and the transfer function form of the incomplete differential is as follows:

G(S)＝U(S)/E(S)＝[(T_αS+1)/(T_fS+1)]*(k_p+k_p/T_iS+k_pT_Ds); wherein kp is a proportionality coefficient, Ti is an integral time constant, TD is a differential time constant, Ta is a series first-order differential element constant, and Tf is a series inertia element constant.

8. The utility model provides a boiler drum water level control system which characterized in that: comprises an acquisition module, an analysis module and a processing module;

the processing module comprises a PI controller and an adaptive fuzzy PID controller; the PI controller is a proportion leading controller; the self-adaptive fuzzy PID controller is a controller for setting P, I, D parameters in real time by using fuzzy reasoning according to an error e and an error change rate ec on the basis of conventional PID control; the acquisition module is used for acquiring the water level of the steam drum, the steam flow and the water supply flow; the analysis module is used for analyzing an error e of the water level according to the steam drum water level, the steam flow and the feed water flow, analyzing an error change rate ec according to the error e and judging whether the error e is larger than a set deviation or not;

the processing unit is used for controlling the opening of a boiler feed water flow valve by adopting a PI controller when the error e is greater than the set deviation; and the processing unit is also used for controlling the opening of the boiler feed water flow valve by adopting an adaptive fuzzy PID controller when the error e is not more than the set deviation.

9. The boiler drum level control system according to claim 8, wherein: adaptive blurringThe input quantity of the PID controller comprises an error e and an error change rate ec, and the output quantity comprises Delta K_p、△K_iAnd Δ K_d(ii) a Obtaining an output quantity according to the input quantity in combination with a preset fuzzy setting rule; the preset fuzzy setting rule is as follows:

when e is greater than the first threshold value, the delta K of the large value area is taken_pAnd Δ K_iIf e × ec<0, taking the Delta K of the median region_d(ii) a If e x ec>0, then taking the delta K of the large value area_d；

When e is greater than the second threshold value and smaller than the first threshold value, taking the delta K of the small value area_pAnd Δ K of median region_i(ii) a If e x ec<0, taking the Delta K of the large value area_d(ii) a If e x ec>0, then taking the delta K of the median region_d；

When e is less than or equal to the second threshold, taking the delta K of the median area_pAnd delta K of small value region_i(ii) a If e x ec<0, taking the Delta K of the small value area_d(ii) a If e x ec>0, taking the Delta K of the median region_d。

10. The boiler drum level control system according to claim 9, wherein: the adaptive fuzzy PID controller also introduces variable domain factors alpha, the actual error basic domains are [ -alpha (E) E, alpha (E) E ], the error change rate basic domains are [ -alpha (EC) EC, alpha (EC) EC ], and the output domains are [ -alpha-delta Kp, ], [ -alpha-delta Ki, alpha-delta Ki ], [ -alpha-delta Kd, -alpha-delta Kd ];

the input domain expansion factor is alpha (x) 1-lambdae^-kx·xWherein, λ ∈ (0,1), k>0; the output domain expansion factor is alpha (x) ═ (| x |/E) tau + epsilon, where tau>0, λ, k, τ, ε are constants.

Technical Field

The invention belongs to the technical field of electric control, and particularly relates to a boiler drum water level control system and method.

Background

Industrial gas boilers are important power plants widely used in the important fields of modern industries such as oil refining, power generation, chemical industry, metallurgy, nuclear industry, and the like. The system is a multi-parameter, strong coupling and large hysteresis system, and the industrial control level is embodied for the control of the gas boiler. The water level of a boiler drum is one of important control quantities of an industrial gas boiler system, and when the water level is too high, steam-water separation is influenced, so that the water content in steam is increased, the quality of the steam is reduced, and the pipe wall of a superheater is scaled when the boiler drum is in the state for a long time, so that equipment is damaged; the boiler is easy to be dry-burned due to too low water level, if the load is increased rapidly, the gasification speed of the boiling water is accelerated, and if the load is not effectively controlled in time, the boiler is easy to be burned out and even explode.

In the control of the boiler, the water level of a boiler drum is an important control parameter, a plurality of disturbance factors exist in the actual work, the false water level is easy to appear, the fluctuation of the water level not only reduces the quality of steam and influences the production, but also can damage equipment when the quality is serious, and endangers the safety of the equipment and workers.

At present, a control method adopted at home and abroad aiming at the steam drum water level is a traditional PID control method, but after the traditional PID control finishes parameter setting, the online adjustment is difficult to carry out, and the steam drum water level of the industrial boiler is frequently influenced by water supply and steam disturbance in the actual working process.

Disclosure of Invention

The invention aims to provide a boiler drum water level control method which can accurately and quickly control the boiler drum water level.

The basic scheme provided by the invention is as follows:

a boiler drum water level control method comprises the following steps:

step one, establishing a PI controller and a self-adaptive fuzzy PID controller; wherein, the PI controller is a proportion leading controller; the self-adaptive fuzzy PID controller is a controller for setting P, I, D parameters in real time by using fuzzy reasoning according to an error e and an error change rate ec on the basis of conventional PID control;

step two, acquiring an error e of the water level at the previous moment, and judging whether the error e is greater than a set deviation; if the value is larger than the preset value, turning to the step three, and if the value is not larger than the preset value, turning to the step four;

step three, controlling the opening of a boiler feed water flow valve by adopting a PI controller, returning to the step one, and controlling the water level at the next moment;

and step four, adopting a self-adaptive fuzzy PID controller to control the opening of a boiler feed water flow valve, and returning to the step one to control the water level at the next moment.

The basic scheme has the following working process and beneficial effects:

when the method is used, a PI controller and an adaptive fuzzy PID controller are established to formally control the water level of the boiler, and after the error e of the water level at the last moment is collected, the current corresponding control method is selected according to whether the error e is greater than the set deviation.

Specifically, if the error e is larger than the set deviation, the adjustment processing needs to be carried out efficiently because the error e is larger, and therefore the opening degree of a boiler feed water flow valve is controlled by adopting a Proportional Integral (PI) controller with a proportional leading function, so that the adjustment processing is fast and complete. If the error e is not larger than the set deviation, the adjustment is focused on the adjustment precision, and therefore, an adaptive fuzzy PID controller is adopted to control the opening degree of the boiler feed water flow valve. On the basis of conventional PID control, the self-adaptive fuzzy PID controller utilizes a controller for real-time setting P, I, D parameters by fuzzy reasoning according to the current error e and the error change rate ec of the system, so that the parameters of the controller can be accurately adjusted in real time, and the accuracy of current adjustment is ensured. And then, repeating the steps to control the water level at the next moment.

In conclusion, the method can accurately and quickly control the water level of the boiler drum.

Further, in the step one, in the built adaptive fuzzy PID controller, the input quantity comprises an error e and an error change rate ec, and the output quantity comprises a delta K_p、△K_iAnd Δ K_d(ii) a And obtaining an output quantity according to the input quantity and a preset fuzzy setting rule.

Has the advantages that: by the arrangement, the output quantity, namely delta K can be set in real time through the preset fuzzy symptom rule_p、△K_iAnd Δ K_dThereby accurately adjusting the parameters of the controller in real time.

Furthermore, in the first step, a domain-variable factor alpha is introduced on the basis of an adaptive fuzzy PID controller, the actual error basic domains are [ -alpha (E) E, alpha (E) E ], the error change rate basic domains are [ -alpha (EC) EC, alpha (EC) EC ], and the output domains are [ -alpha Δ Kp, alpha Δ Kp ], [ -alpha Δ Ki, alpha Δ Ki ], [ -alpha Δ Kd, -alpha Δ Kd ];

the input domain expansion factor is alpha (x) 1-lambdae^-kx·xWherein, λ ∈ (0,1), k>0; the output domain expansion factor is alpha (x) ═ (| x |/E) tau + epsilon, where tau>0, λ, k, τ, ε are constants.

Has the advantages that: by the arrangement, the control domain of discourse can be changed along with large change, and the accuracy of the result of real-time adjustment is ensured. The specific values of λ, k, τ, and ε can be set by one skilled in the art according to the requirements of the application.

Furthermore, the input error e, the error change rate ec and the output delta K of the adaptive fuzzy PID controller_p、△K_i、△K_dThe language variable values are all negative big, negative middle, negative small, zero, positive small, middle and positive big, and the discourse domain is set as [ -6, 6]The quantization levels are { -6, -5, -4, -3, -2, -1, 0,1, 2, 3, 4, 5, 6} and the membership functions of the fuzzy subsets of all linguistic variables are triangular membership functions.

Has the advantages that: the parameter definition mode can ensure the accuracy and the whole adjustment efficiency.

Further, the fuzzy setting rule of the parameters in the self-adaptive fuzzy PID controller is as follows:

according to the value range, respectively is Δ K_p、△K_iAnd Δ K_dSetting a large value area, a medium value area and a small value area;

when e is greater than the first threshold value, the delta K of the large value area is taken_pAnd Δ K_iIf e × ec<0, taking the Delta K of the median region_d(ii) a If e x ec>0, then taking the delta K of the large value area_d；

When e is greater than the second threshold value and smaller than the first threshold value, taking the delta K of the small value area_pAnd Δ K of median region_i(ii) a If e x ec<0, taking the Delta K of the large value area_d(ii) a If e x ec>0, then taking the delta K of the median region_d；

When e is less than or equal to the second threshold valueTaking the Delta K of the median region_pAnd delta K of small value region_i(ii) a If e x ec<0, taking the Delta K of the small value area_d(ii) a If e x ec>0, taking the Delta K of the median region_d。

Has the advantages that: according to the fuzzy setting rule, the output quantity can be selected in a targeted manner according to the specific conditions of the error e and the error change rate ec, and the effectiveness of regulation is ensured.

Further, in the fourth step, when the self-adaptive fuzzy PID model is adopted to control the opening degree of the boiler feed water flow valve, firstly, the PID setting method based on the optimization function is utilized to obtain an initial parameter K_p0、K_i0、K_d0Then obtaining the variable quantity delta K of each parameter by fuzzy reasoning of a self-adaptive fuzzy PID model_p、△K_i、△K_dSuperposing the parameter variation and the initial parameter to obtain a real-time setting K_p、K_i、K_dAnd after the parameters are obtained, the output quantity of the controller is obtained, and the opening of the valve is controlled according to the output quantity of the controller.

Has the advantages that: by the arrangement, the consistency and the stability of the effect of the method can be ensured under the condition of long-time work.

Further, in the fourth step, an incomplete differential element is further included, and a transfer function form of the incomplete differential is as follows:

G(S)＝U(S)/E(S)＝[(T_αS+1)/(T_fS+1)]*(k_p+k_p/T_iS+k_pT_Ds); wherein kp is a proportionality coefficient, Ti is an integral time constant, TD is a differential time constant, Ta is a series first-order differential element constant, and Tf is a series inertia element constant.

Has the advantages that: the essence of incomplete differentiation is that a low-pass filter is added on the basis of a PID controller, namely a first-order differentiation link; however, the existing incomplete differentiation cannot be combined with fuzzy PID in the system operation process, and the setting parameter of the adaptive algorithm is only K_p、K_iAnd the control of the system on the error change rate is reduced. Compared with the prior art, the improved incomplete differential can realize on-line setting of P, I, D parametersMeanwhile, the error change rate can be reasonably restrained, and the function of incomplete differentiation is combined to restrain oscillation.

Another objective of the present application is to provide a boiler drum water level control system, which is applied to the boiler drum water level control method, and includes an acquisition module, an analysis module and a processing module;

the processing module comprises a PI controller and an adaptive fuzzy PID controller; the PI controller is a proportion leading controller; the self-adaptive fuzzy PID controller is a controller for setting P, I, D parameters in real time by using fuzzy reasoning according to an error e and an error change rate ec on the basis of conventional PID control; the acquisition module is used for acquiring the water level of the steam drum, the steam flow and the water supply flow; the analysis module is used for analyzing an error e of the water level according to the steam drum water level, the steam flow and the feed water flow, analyzing an error change rate ec according to the error e and judging whether the error e is larger than a set deviation or not;

the processing unit is used for controlling the opening of a boiler feed water flow valve by adopting a PI controller when the error e is greater than the set deviation; and the processing unit is also used for controlling the opening of the boiler feed water flow valve by adopting an adaptive fuzzy PID controller when the error e is not more than the set deviation.

Has the advantages that: when the method is used, a PI controller and an adaptive fuzzy PID controller are established to formally control the water level of the boiler, and after the error e of the water level at the last moment is collected, the current corresponding control method is selected according to whether the error e is greater than the set deviation. Thereby accurately and quickly controlling the water level of the boiler drum.

Further, the input quantity of the adaptive fuzzy PID controller comprises an error e and an error change rate ec, and the output quantity comprises a delta K_p、△K_iAnd Δ K_d(ii) a Obtaining an output quantity according to the input quantity in combination with a preset fuzzy setting rule; the preset fuzzy setting rule is as follows:

when e is greater than the first threshold value, the delta K of the large value area is taken_pAnd Δ K_iIf e × ec<0, taking the Delta K of the median region_d(ii) a If e x ec>0, then taking the delta K of the large value area_d；

When |. e | > is greater than the firstWhen the two threshold values are smaller than the first threshold value, the delta K of the small value area is taken_pAnd Δ K of median region_i(ii) a If e x ec<0, taking the Delta K of the large value area_d(ii) a If e x ec>0, then taking the delta K of the median region_d；

When e is less than or equal to the second threshold, taking the delta K of the median area_pAnd delta K of small value region_i(ii) a If e x ec<0, taking the Delta K of the small value area_d(ii) a If e x ec>0, taking the Delta K of the median region_d。

Has the advantages that: according to the fuzzy setting rule, the output quantity can be selected in a targeted manner according to the specific conditions of the error e and the error change rate ec, and the effectiveness of regulation is ensured.

Furthermore, the adaptive fuzzy PID controller also introduces a variable domain factor alpha, the actual error basic domains are [ -alpha (E) E, alpha (E) E ], the error change rate basic domains are [ -alpha (EC) EC, alpha (EC) EC ], and the output domains are [ -alpha delta Kp, ], [ -alpha delta Ki, alpha delta Ki ], [ -alpha delta Kd, -alpha delta Kd ];

the input domain expansion factor is alpha (x) 1-lambdae^-kx·xWherein, λ ∈ (0,1), k>0; the output domain expansion factor is alpha (x) ═ (| x |/E) tau + epsilon, where tau>0, λ, k, τ, ε are constants.

Has the advantages that: by the arrangement, the control domain of discourse can be changed along with large change, and the accuracy of the result of real-time adjustment is ensured. The specific values of λ, k, τ, and ε can be set by one skilled in the art according to the requirements of the application.

Through simulation analysis, the maximum overshoot of the traditional PID controller is about 50%, and the regulation time is about 4.2 seconds. The maximum overshoot of the controller designed by the project does not exceed 13%, and the step response of the self-adaptive controller with the incomplete differential link does not overshoot; the adjusting time is less than 2 seconds, wherein the adjusting time of the self-adaptive controller with the incomplete differential link is 0.7 second. To sum up, the dynamic and static performance of this application all is superior to traditional PID control by a wide margin, control boiler drum water level that can be accurate and quick.

Drawings

FIG. 1 is a flow chart of a boiler drum water level control method according to an embodiment of the present invention;

FIG. 2 is a logic block diagram of a boiler drum level control system in accordance with an embodiment of the present invention;

FIG. 3 is a simulation comparison diagram according to a first embodiment of the present invention;

fig. 4 is a schematic diagram of an incomplete differential element in an embodiment of the present invention.

Detailed Description

The following is further detailed by the specific embodiments:

example one

As shown in fig. 1, a method for controlling a water level of a boiler drum includes:

step one, establishing a PI controller and a self-adaptive fuzzy PID controller; wherein, the PI controller is a proportion leading controller;

and (3) optimizing the parameter weight of the PI controller, namely determining a group of proper Kp and Ki parameters to ensure that a system control index reaches the optimum, wherein an integral taking a control system instantaneous error e (t) as a functional is usually adopted as an evaluation index, and commonly used evaluation indexes are ISAE, ITAE, IAE, ISE, ISTE and IST 2E. After simulation research is carried out on the 6 performance index functions, the inventor adopts an IST2E index as a parameter setting criterion of the controller, optimizes the parameter weight of the PI controller by combining a nonlinear control system optimization design algorithm, obtains the optimal control parameter, and improves the system rapidity.

The self-adaptive fuzzy PID controller is a controller for setting P, I, D parameters in real time by fuzzy reasoning according to the current error e and the error change rate ec of the system on the basis of conventional PID control. In the adaptive fuzzy PID controller, the input quantity includes error e and error change rate ec, and the output quantity includes DeltaK_p、△K_iAnd Δ K_d(ii) a And obtaining an output quantity according to the input quantity and a preset fuzzy setting rule.

And introducing a variable universe factor alpha on the basis of an adaptive fuzzy PID controller, wherein the actual error basic universe is [ -alpha (E) E, alpha (E) E]The error rate of change is [ -alpha (EC) EC, alpha (EC) EC]The output domain is [ -alpha-delta Kp,]、[-α△Ki,α△Ki]、[-α△Kd,-α△Kd](ii) a The input domain expansion factor is alpha (x) 1-lambdae^-kx·xWherein, λ ∈ (0,1), k>0; the output domain expansion factor is alpha (x) ═ (| x |/E) tau + epsilon, where tau>0, λ, k, τ, ε are constants. The specific values of λ, k, τ, and ε can be set by one skilled in the art according to the requirements of the application.

Wherein, the input error e, the error change rate ec and the output delta K of the adaptive fuzzy PID controller_p、△K_i、△K_dThe language variable values are all negative big, negative middle, negative small, zero, positive small, middle and positive big, and the discourse domain is set as [ -6, 6]The quantization levels are { -6, -5, -4, -3, -2, -1, 0,1, 2, 3, 4, 5, 6} and the membership functions of the fuzzy subsets of all linguistic variables are triangular membership functions.

The fuzzy setting rule of the parameters in the self-adaptive fuzzy PID controller is as follows:

according to the value range, respectively is Δ K_p、△K_iAnd Δ K_dSetting a large value area, a medium value area and a small value area;

when e is greater than the first threshold value, the delta K of the large value area is taken_pAnd Δ K_iIf e × ec<0, then taking the delta K of the median region_d(ii) a If e x ec>0, then taking the delta K of the large value area_d；

When e is greater than the second threshold value and smaller than the first threshold value, the small value area has a small delta K for a system response with a small overshoot_pDelta K of median region_i(ii) a If e x ec<0, then taking the delta K of the large value area_d(ii) a If e x ec>0, the influence of the value of Kd on the system response is large, and the delta K of a median region is taken_d；

When e is less than or equal to the second threshold, delta K of the median area is used to shorten the adjustment time of the system_pAnd delta K of small value region_i(ii) a If e x ec<0, then take the delta K of the small value area_d(ii) a If e x ec>0, taking the Delta K of the median region_d。

Step two, acquiring an error e of the water level at the previous moment, and judging whether the error e is greater than a set deviation; if the value is larger than the preset value, turning to the step three, and if the value is not larger than the preset value, turning to the step four; in this embodiment, the deviation is set to 30%, and such a value can ensure the accuracy and the overall efficiency of the adjustment.

Step three, controlling the opening of a boiler feed water flow valve by adopting a PI controller, returning to the step one, and controlling the water level at the next moment; to improve the rapidity of the system.

Step four, adopting a self-adaptive fuzzy PID controller to control the opening of a boiler feed water flow valve, and returning to the step one to control the water level at the next moment; ensuring the accuracy of the system.

The working process of the self-adaptive fuzzy PID controller comprises the following steps: firstly, an initial parameter K is obtained by utilizing a PID setting method based on an optimization function_p0、K_i0、K_d0Then obtaining the variable quantity delta K of each parameter by fuzzy reasoning of a self-adaptive fuzzy PID model_p、△K_i、△K_dSuperposing the parameter variation and the initial parameter to obtain a real-time setting K_p、K_i、K_dAnd after the parameters are obtained, the output quantity of the controller is obtained, and the opening of the valve is controlled according to the output quantity of the controller. In this embodiment, the PID tuning method of the optimization function is the IST2E criterion.

As shown in fig. 2, the invention further provides a boiler drum water level control system, which is applied to the boiler drum water level control method and comprises an acquisition module, an analysis module and a processing module. The acquisition module adopts the existing acquisition device for the water level of the boiler drum. The analysis module and the processing module are integrated at a control end, and the control end is an industrial PC in the embodiment.

The processing module comprises a PI controller and an adaptive fuzzy PID controller; the PI controller is a proportion leading controller; the self-adaptive fuzzy PID controller is a controller for setting P, I, D parameters in real time by using fuzzy reasoning according to an error e and an error change rate ec on the basis of conventional PID control; the acquisition module is used for acquiring the water level of the steam drum, the steam flow and the water supply flow; the analysis module is used for analyzing an error e of the water level according to the steam drum water level, the steam flow and the feed water flow, analyzing an error change rate ec according to the error e and judging whether the error e is larger than a set deviation or not;

the processing unit is used for controlling the opening of a boiler feed water flow valve by adopting a PI controller when the error e is greater than the set deviation; and the processing unit is also used for controlling the opening of the boiler feed water flow valve by adopting an adaptive fuzzy PID controller when the error e is not more than the set deviation.

Wherein, the input quantity of the adaptive fuzzy PID controller comprises an error e and an error change rate ec, and the output quantity comprises a delta K_p、△K_iAnd Δ K_d(ii) a Obtaining an output quantity according to the input quantity in combination with a preset fuzzy setting rule; the preset fuzzy setting rule is as follows:

when e is greater than the first threshold value, the delta K of the large value area is taken_pAnd Δ K_iIf e × ec<0, taking the Delta K of the median region_d(ii) a If e x ec>0, then taking the delta K of the large value area_d(ii) a When e is greater than the second threshold value and smaller than the first threshold value, taking the delta K of the small value area_pAnd Δ K of median region_i(ii) a If e x ec<0, taking the Delta K of the large value area_d(ii) a If e x ec>0, then taking the delta K of the median region_d(ii) a When e is less than or equal to the second threshold, taking the delta K of the median area_pAnd delta K of small value region_i(ii) a If e x ec<0, taking the Delta K of the small value area_d(ii) a If e x ec>0, taking the Delta K of the median region_d。

The adaptive fuzzy PID controller also introduces variable universe factor alpha, and the actual error basic universe is [ -alpha (E) E, alpha (E) E]The error rate of change is [ -alpha (EC) EC, alpha (EC) EC]The output domain is [ -alpha-delta Kp,]、[-α△Ki,α△Ki]、[-α△Kd,-α△Kd](ii) a The input domain expansion factor is alpha (x) 1-lambdae^-kx·xWherein, λ ∈ (0,1), k>0; the output domain expansion factor is alpha (x) ═ (| x |/E) tau + epsilon, where tau>0, λ, k, τ, ε are constants.

By using the scheme, when a PI controller and a self-adaptive fuzzy PID controller are established to formally control the water level of the boiler, after the error e of the water level at the last moment is collected, the current corresponding control method can be selected according to whether the error e is greater than the set deviation. Specifically, if the error e is larger than the set deviation, the adjustment processing needs to be carried out efficiently because the error e is larger, and therefore the opening degree of a boiler feed water flow valve is controlled by adopting a Proportional Integral (PI) controller with a proportional leading function, so that the adjustment processing is fast and complete. If the error e is not larger than the set deviation, the adjustment is focused on the adjustment precision, and therefore, an adaptive fuzzy PID controller is adopted to control the opening degree of the boiler feed water flow valve. On the basis of conventional PID control, the self-adaptive fuzzy PID controller utilizes a controller for real-time setting P, I, D parameters by fuzzy reasoning according to the current error e and the error change rate ec of the system, so that the parameters of the controller can be accurately adjusted in real time, and the accuracy of current adjustment is ensured. And then, repeating the steps to control the water level at the next moment.

As shown in fig. 3, through simulation analysis, the maximum overshoot of the conventional PID controller is about 50%, and the tuning time is about 4.2 seconds. According to the technical scheme, the maximum overshoot does not exceed 13%, wherein the step response of the adaptive controller is free of overshoot; the adjusting time is less than 2 seconds, wherein the adjusting time of the adaptive controller is 0.7 second. To sum up, the dynamic and static performance of this application all is superior to traditional PID control by a wide margin, control boiler drum water level that can be accurate and quick.

Example two

Different from the first embodiment, as shown in fig. 4, in the fourth step of the boiler drum water level control method according to the present embodiment, an incomplete differential link is further included, and the incomplete differential link is combined with a self-adaptive fuzzy PID to control the system, so that the system can perform online self-tuning on parameters, and simultaneously, the incomplete differential link is used to suppress system oscillation, so as to improve the dynamic and static performance of the system. The transfer function form of the incomplete differential is:

G(S)＝U(S)/E(S)＝[(T_αS+1)/(T_fS+1)]*(k_p+k_p/T_iS+k_pT_Ds); wherein k is_pIs a proportionality coefficient, T_iTo integrate the time constant, T_DIs a differential time constant, T_αIs a series first order differential element constant, T_fIs a series inertia element constant.

The essence of incomplete differentiation is to add on the basis of a PID controllerA low-pass filter, i.e. a first order differential element; however, the existing incomplete differentiation cannot be combined with fuzzy PID in the system operation process, and the setting parameter of the adaptive algorithm is only K_p、K_iAnd the control of the system on the error change rate is reduced. Compared with the prior art, the improved incomplete differential can realize on-line setting of P, I, D parameters, can reasonably restrain error change rate and has the function of incomplete differential to restrain oscillation. Therefore, the on-line self-tuning of the control parameters can be realized, and the problem of quality reduction caused by signal mutation on the system can be solved, so that the control quality is improved.

EXAMPLE III

Different from the first embodiment, in the present embodiment, a heat recovery device is disposed on a connection pipeline between the boiler and the steam drum, and the heat recovery device is configured to absorb heat energy of the connection pipeline and convert the heat energy into electric energy to be stored in a temporary power supply; a liquid dropping pipe is fixedly arranged on the side wall of the steam drum, penetrates through the wall of the steam drum and is in sealed connection with the wall of the steam drum; the liquid outlet end of the liquid dropping pipe is arranged in the steam drum, the other end of the liquid dropping pipe is connected with a delivery pump, the delivery pump is connected with a cleaning barrel, and cleaning liquid is filled in the cleaning barrel; the delivery pump is respectively electrically connected with the control end and the temporary power supply; a small vibration motor is fixed on the outer wall of the descending pipe of the steam pocket and is respectively and electrically connected with the control end and the temporary power supply;

if the water level is continuously greater than the set deviation for N times and the water level is higher than the set deviation, the control end controls the conveying pump to work for a first working time and controls the small vibration motor to work for a second preset time.

The specific working process is as follows:

in the process of delivering water to the steam pocket by the boiler, the temperature of the water is higher, the connecting pipe is basically a metal pipeline, the specific heat capacity is low, and the heat waste is more. In this scheme, through be equipped with heat recovery unit on connecting tube, heat recovery unit absorbs connecting tube's heat energy transformation and becomes electric energy storage in temporary power source, can the effectual heat that utilizes in the transmission course.

If the water level is continuously greater than the set deviation for N times and the water level is higher in the working process, the situation that the downcomer of the steam pocket is not smooth enough is shown, and in other words, slight blockage occurs. If not processed in time, the working efficiency of the boiler drum can be influenced, and the efficiency of the whole device is further influenced. Therefore, the control end controls the conveying pump to work for a first working time period and controls the small vibration motor to work for a second preset time period. Send into the washing liquid in to the steam pocket through control delivery pump work, can wash the downtake, simultaneously, small-size shock dynamo shakes, can mix the washing liquid with the water of pipeline down on the one hand and even with the family, and on the other hand then can make the plug in the sewer scattered through the mode of vibrations, guarantees abluent effect. In this way, the downcomer can be disposed of in time when it becomes slightly clogged. Besides, the processed electric energy comes from the heat recovered from the connecting pipeline, and an energy structure is not required to be specially arranged, so that the energy can be saved. Since the situation of slight blockage of the downcomer is not frequent, the efficiency of storing electric energy is sufficient to meet daily requirements.

The foregoing is merely an example of the present invention, and common general knowledge in the field of known specific structures and characteristics is not described herein in any greater extent than that known in the art at the filing date or prior to the priority date of the application, so that those skilled in the art can now appreciate that all of the above-described techniques in this field and have the ability to apply routine experimentation before this date can be combined with one or more of the present teachings to complete and implement the present invention, and that certain typical known structures or known methods do not pose any impediments to the implementation of the present invention by those skilled in the art. It should be noted that, for those skilled in the art, without departing from the structure of the present invention, several changes and modifications can be made, which should also be regarded as the protection scope of the present invention, and these will not affect the effect of the implementation of the present invention and the practicability of the patent. The scope of the claims of the present application shall be determined by the contents of the claims, and the description of the embodiments and the like in the specification shall be used to explain the contents of the claims.