CN114167717B - Thermal power unit DEH rotating speed control method based on improved PSO-fuzzy PID - Google Patents
Thermal power unit DEH rotating speed control method based on improved PSO-fuzzy PID Download PDFInfo
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Abstract
The invention discloses a thermal power unit DEH rotating speed control method based on improved PSO-fuzzy PID, which comprises the following steps: constructing a DEH system of the thermal power generating unit; establishing a fuzzy PID control module; introducing an improved PSO algorithm to optimize a fuzzy PID control module, adopting the improved PSO algorithm to optimize a scale factor and a quantization factor in the fuzzy PID control module, sending an optimal value into the fuzzy PID control module, adopting the improved PSO algorithm to take an error signal of a DEH system of the thermal power generating unit as an adaptability function, and setting inertia weight of power exponential decay; the improved PSO-fuzzy PID control module is applied to the thermal power unit DEH system to form closed loop feedback, so that the rotational speed control of the thermal power unit DEH system is realized. In the improved PSO algorithm, the error signal of the DEH system of the thermal power generating unit is used as a fitness function, and the inertial weight of power exponential decay is set, so that the method has the advantages of fast convergence, good stability for system control, fast response time, good system robustness and the like.
Description
Technical Field
The invention relates to the technical field of thermal power unit control, in particular to a thermal power unit DEH rotating speed control method based on improved PSO-fuzzy PID.
Background
The turbine plays an important role in the power generation process of the thermal power unit, and is a rotary steam power device, high-temperature and high-pressure steam passes through a fixed nozzle to become accelerated airflow and then is sprayed onto blades, a rotor provided with a blade row rotates after receiving the airflow, and meanwhile, a generator is driven to rotate to generate electric energy. The DEH rotation speed control of the steam turbine is an important link in the power generation process of the thermal power generating unit, and a traditional PID control method is generally used. The traditional PID control method is based on the experience of engineers to manually adjust the parameters of PID, so that the result error is larger and the control effect is not ideal. Along with the increasing national requirements on the safety of power plants, the requirements on the operation accuracy of the nuclear power plants are also increasing, and the requirements on the control systems of the thermal power unit turbines with large installed capacity and high parameters are also increasing. Conventional trial-and-error methods based on experience have failed to meet the current requirements for more precise control, have many safety hazards, and do not allow the operating engineer to perform frequent trial-and-error during actual operation. In view of the fact that the automation degree of the controller is not high enough, the development of the novel thermal power unit DEH controller has important application value.
The study of process control systems is often based on mathematical models of transfer functions, however, actual thermal power plant systems are large complex systems in which there are many time-varying uncertainties and nonlinearities that are difficult to model accurately. In view of the advantages of fuzzy control, such as no need of accurate mathematical model and strong robustness, the system is controlled by the fuzzy PID controller. The fuzzy PID algorithm requires an expert to give a fuzzy rule, and errors exist in the parameter adjustment process.
Disclosure of Invention
The invention aims to: aiming at the defect of larger parameter condition error when a fuzzy PID controller is adopted to control the thermal power unit DEH in the prior art, the invention controls the rotating speed of the thermal power unit DEH by improving PSO-fuzzy PID, takes an error signal of a thermal power unit DEH system as a fitness function and sets inertia weight of power exponential decay in an improved PSO algorithm, and has the advantages of fast convergence, good stability to system control, fast response time, good system robustness and the like.
The technical scheme is as follows: in order to achieve the technical purpose, the invention adopts the following technical scheme.
A thermal power generating unit DEH rotating speed control method based on improved PSO-fuzzy PID comprises the following steps:
s1, constructing a thermal power unit DEH system: constructing a DEH system of the thermal power unit, which mainly comprises an electrohydraulic converter, a steam turbine, an oil motor, a transmission mechanism, a steam volume and a reheating volume;
s2, establishing a fuzzy PID control module: the method comprises the steps of constructing a fuzzy PID control module, wherein the fuzzy controller is used for adjusting control parameters of the fuzzy PID control module, an input signal is a rotating speed deviation and a rotating speed deviation change rate, and an output signal is an adjusting variable of the PID controller;
S3, establishing an improved PSO-fuzzy PID control module: introducing an improved PSO algorithm to optimize a fuzzy PID control module, adopting the improved PSO algorithm to optimize a scale factor and a quantization factor in the fuzzy PID control module, sending an optimal value into the fuzzy PID control module, adopting the improved PSO algorithm to take an error signal of a DEH system of the thermal power generating unit as an adaptability function, and setting inertia weight of power exponential decay;
S4, controlling the rotating speed of a DEH system of the thermal power generating unit: and (3) applying the improved PSO-fuzzy PID control module in the step (S3) to the thermal power unit DEH system in the step (S1) to form closed loop feedback, so as to realize the rotation speed control of the thermal power unit DEH system.
Preferably, the calculation formula of the inertial weight of the power exponential decay is:
ω＝αE-βE
wherein ω is an improved PSO inertial weight factor; alpha E -βE is a power exponent function, E is the iteration number, and alpha and beta are positive numbers.
Preferably, the fitness function has a calculation formula:
wherein J is a fitness function, e (t) is a control deviation signal, namely an error signal of a DEH system of the thermal power unit, and t is a time.
Preferably, in the improved PSO algorithm, the particle motion space dimension is 5, which corresponds to the scale factor K a、Kb、Xc and the quantization factor K e、Kec in the fuzzy PID controller respectively; the velocity update formula and the position update formula of the particles are respectively:
vi(k+1)＝vi(k)+c1r1i(pi(k)-xi(k))+c2r2i(pg(k)-xi(k))
xi(k+1)＝xi(k)+vi(k+1)
Wherein c 1 and c 2 are acceleration factors for improving PSO, r 1i and r 2i are random numbers of particle i in the (0, 1) range in the iterative process, i=1, 2,3,4,5, respectively; v i (k) and v i (k+1) are the velocities of particle i in the kth and k+1th iterations, respectively; x i (k) and x i (k+1) are the positions of particle i in the kth and kth+1th iterations, respectively, and p i (k) is the optimal position searched so far for particle i; p g (k) is the optimum position searched so far for the entire population of particles.
Preferably, r 1i and r 2i are re-valued in each iteration.
Preferably, in the fuzzy controller in the step S2, the input signal is a rotational speed deviation e and a rotational speed deviation change rate ec, the output signal is a regulating variable K p、Ki、Kd of the PID controller, the quantization factor is K e、Kec, and the scale factor is K a、Kb、Kc; the rotational speed deviation e and the rotational speed deviation change rate ec set the subsets as: { Negative Big (NB), negative Medium (NM), negative Small (NS), zero (ZO), positive Small (PS), median (PM), positive Big (PB) }, ΔK p、ΔKi、ΔKd sets the subset to { B, M, S }; the input and the output are set to trimf; control rules are set and the rule table is expressed in the form of if-then.
Preferably, the mathematical model of the DEH system of the thermal power generating unit in the step S1 is:
Wherein G 1(S)、G2(S)、G3(S)、G4 (S) is a transfer function model of an electrohydraulic converter, an oil engine, a steam volume, and a reheat volume, T y is an electrohydraulic converter time constant, T c is an oil engine time constant, T CH is a high pressure steam chamber steam volume time constant, T RH is a reheat steam volume time constant, and T 0 =1.5 is a delay time of a reheat system.
The beneficial effects are that: according to the invention, the rotating speed of the thermal power unit DEH is controlled by improving the PSO-fuzzy PID, in the improved PSO algorithm, an error signal of the thermal power unit DEH system is used as a fitness function, and the inertial weight of power exponential decay is set, so that the method has the advantages of fast convergence, good stability of system control, fast response time, good system robustness and the like, and is suitable for controlling the rotating speed of the thermal power unit DEH.
Drawings
FIG. 1 is a flow chart of the method of the present invention;
FIG. 2 is a schematic diagram of a simulation structure;
FIG. 3 is a schematic diagram of a simulated concrete structure of the DEH system of the thermal power generating unit in FIG. 2;
FIG. 4 is a schematic block diagram of a fuzzy PID control module according to the present invention;
FIG. 5 is a schematic block diagram of an improved PSO-fuzzy PID control module according to the present invention;
FIG. 6 is a graph comparing the improved PSO algorithm P-EXPPSO of the present invention with the tests of LDWPSO, CFPSO in Sphere function;
FIG. 7 is a graph comparing the improved PSO algorithm P-EXPPSO of the present invention with the tests of LDWPSO, CFPSO in Rosenbrock function;
FIG. 8 is a graph comparing the improved PSO algorithm P-EXPPSO of the present invention with the tests of LDWPSO, CFPSO in RASTRIGIN function;
FIG. 9 is a flowchart illustrating the operation of the improved PSO-fuzzy PID control module of the present invention;
FIG. 10 is an iterative plot of quantization factors for an improved PSO-fuzzy PID control module according to the invention;
FIG. 11 is an iterative plot of the scaling factor of the improved PSO-fuzzy PID control module of the present invention;
FIG. 12 is an iterative plot of the adaptation function value of the improved PSO-fuzzy PID control module of the present invention;
FIG. 13 is a graph of the output of the improved PSO-fuzzy PID control module of FIG. 2 with a conventional PID controller, a fuzzy PID controller, applied to a DEH system of a thermal power plant.
Detailed Description
The invention relates to a thermal power generating unit DEH rotating speed control method based on improved PSO-fuzzy PID, which is further described and explained with reference to the accompanying drawings and the embodiment. Wherein, DEH: digital Electronic Hydraulic, a digital electrohydraulic regulating system.
As shown in the attached figure 1, the method for controlling the DEH rotating speed of the thermal power unit based on the improved PSO-fuzzy PID comprises the following steps:
S1, constructing a thermal power unit DEH system: constructing a DEH system of the thermal power unit, which mainly comprises an electrohydraulic converter, a steam turbine, an oil motor, a transmission mechanism, a steam volume and a reheating volume; the mathematical model of the DEH system of the thermal power generating unit is as follows:
Wherein G 1(S)、G2(S)、G3(S)、G4 (S) is a transfer function model of an electrohydraulic converter, an oil engine, a steam volume, and a reheat volume, T y is an electrohydraulic converter time constant, T c is an oil engine time constant, T CH is a high pressure steam chamber steam volume time constant, T RH is a reheat steam volume time constant, and T 0 =1.5 is a delay time of a reheat system.
S2, establishing a fuzzy PID control module: the method comprises the steps of constructing a fuzzy PID control module, wherein the fuzzy controller is used for adjusting control parameters of the fuzzy PID control module, an input signal is a rotating speed deviation and a rotating speed deviation change rate, and an output signal is an adjusting variable of the PID controller;
In the fuzzy controller, an input signal is a rotation speed deviation e and a rotation speed deviation change rate ec, an output signal is a regulating variable K p、Ki、Kd of a PID controller, a quantization factor is K e、Kec, and a scale factor is K a、Kb、Kc; the rotational speed deviation e and the rotational speed deviation change rate ec set the subsets as: { Negative Big (NB), negative Medium (NM), negative Small (NS), zero (ZO), positive Small (PS), median (PM), positive Big (PB) }, ΔK p、ΔKi、ΔKd sets the subset to { B, M, S }; the input and the output are set to trimf; control rules are set and the rule table is expressed in the form of if-then.
S3, establishing an improved PSO-fuzzy PID control module: introducing an improved PSO algorithm to optimize a fuzzy PID control module, adopting the improved PSO algorithm to optimize a scale factor and a quantization factor in the fuzzy PID control module, sending an optimal value into the fuzzy PID control module, adopting the improved PSO algorithm to take an error signal of a DEH system of the thermal power generating unit as an adaptability function, and setting inertia weight of power exponential decay;
the calculation formula of the inertial weight of the power exponential decay is as follows:
ω＝αE-βE
wherein ω is an improved PSO inertial weight factor; alpha E -βE is a power exponent function, E is the iteration number, and alpha and beta are positive numbers.
The fitness function is calculated by the following formula:
wherein J is a fitness function, e (t) is a control deviation signal, namely an error signal of a DEH system of the thermal power unit, and t is a time.
In the improved PSO algorithm, the dimension of the particle motion space is 5, and the dimension corresponds to a scale factor K a、Kb、Kc and a quantization factor K e、Kec in the fuzzy PID controller respectively; the velocity update formula and the position update formula of the particles are respectively:
vi(k+1)＝vi(k)+c1r1i(pi(k)-xi(k))+c2r2i(pg(k)-xi(k))
xi(k+1)＝xi(k)+vi(k+1)
Wherein c 1 and c 2 are acceleration factors for improving PSO, r 1i and r 2i are random numbers of particle i in the (0, 1) range in the iterative process, i=1, 2,3,4,5, respectively; v i (k) and v i (k+1) are the velocities of particle i in the kth and k+1th iterations, respectively; x i (k) and x i (k+1) are the positions of particle i in the kth and kth+1th iterations, respectively, and p i (k) is the optimal position searched so far for particle i; p g (k) is the optimum position searched so far for the entire population of particles.
S4, controlling the rotating speed of a DEH system of the thermal power generating unit: the improved PSO-fuzzy PID control module in the step S3 is applied to the thermal power unit DEH system in the step S1 to form closed loop feedback, so that the rotational speed control of the thermal power unit DEH system is realized
In the improved PSO algorithm, the error signal of the DEH system of the thermal power generating unit is used as a fitness function, and the inertial weight of power exponential decay is set, so that the method has the advantages of fast convergence, good stability for system control, fast response time, good system robustness and the like.
Simulation verification:
As shown in fig. 2, the simulation system includes: a ramp signal unit; the adaptive function module of the improved PSO algorithm consists of a clock module, a product operation module x and an integration module 1/s; the traditional PID module is composed of an amplifier module, an integration module 1/s, a differential module and an adder; the amplifier, the fuzzy controller, the product operation module x, the adder and the traditional PID module are combined to form a fuzzy PID module; a thermal power unit DEH system model module consisting of a transfer function module, an adder, a white noise module and a delay module is used for creating a subsystem, as shown in figure 3; and an oscilloscope display unit for signal comparison.
In the bottom part of fig. 2, a given ramp signal unit outputs a ramp signal as an input signal, the input signal sequentially passes through a traditional PID control module and a subsystem of a system model module to generate an output signal, the output signal is used as a negative feedback signal, and an error signal e (t) formed by the negative feedback signal and the input signal is sent to a traditional PID controller to form a closed-loop traditional PID control system; in the middle part of the figure 2, a slope signal unit outputs a slope signal as an input signal, the input signal sequentially passes through a fuzzy PID control module and a subsystem of a system model module to generate an output signal, the output signal is used as a negative feedback signal, and an error signal e (t) formed by the negative feedback signal and the input signal is sent to a fuzzy PID controller to form a closed-loop fuzzy PID control system; as shown in the top part of the figure 2, the error signal e (t) of the same closed-loop fuzzy PID control system is simultaneously sent to the adaptive function module, the fuzzy PID parameters are optimized by the improved PSO algorithm, and the optimized parameters are sent to the fuzzy PID controller to form the control system for optimizing the fuzzy PID parameters by the improved PSO algorithm. And the output of the three-part control system is connected with a comparative oscilloscope display unit to form the whole simulation control system.
For the design of the convenient controller, simplify the DEH system of the thermal power generating unit, and consist of an electrohydraulic converter, a steam turbine, an oil motor, a transmission mechanism, a steam volume and a reheat volume. Besides the delay of the reheating system, the input and output of each part have certain inertia, and the output is stable, so that inertia links are adopted.
As shown in fig. 3, the modeling process of the DEH system of the thermal power generating unit in the invention is as follows: the rotational speed deviation is converted into an electrical signal by the regulating controller. In the DEH system of the thermal power unit, an electro-hydraulic converter receives an electric signal and sends the electric signal to a steam turbine, an oil pressure difference is generated to enable a slide valve to shift, and a mathematical model is shown as a formula (1):
Wherein G 1 (S) is a transfer function model of the electrohydraulic converter, and T y =0.2 is a electrohydraulic converter time constant (typically 0.01 to 0.9).
The slide valve is displaced to drive the piston of the oil motor to move, and the air adjusting valve is driven by the transmission mechanism. The mathematical model is shown as (2):
wherein, S h is the displacement of the slide valve, T c is the time constant of the engine, and sigma is the motion variable of the engine. The transfer function is in the form of equation (3):
Wherein G 2 (S) is a transfer function model of the engine, and T c =0.3 can be adjusted according to the requirement.
The steam flow is regulated by an air valve, and the steam is expanded and does work through a high-pressure cylinder and an intermediate reheating pipe. The mathematical model is shown as the formula (4-6).
Where G 3 (S) is a transfer function model of the steam volume, T CH =0.35 is a high pressure steam chamber steam volume time constant (typically 0.3 to 0.5).
Where G 4 (S) is a transfer function model of the reheat volume, T RH =8 is a reheat steam volume time constant, and T 0 =1.5 is a reheat volume delay time.
And adding a white noise generator and an adder as interference signals of the steam pressure disturbance, wherein the sampling time is set to be 0.1.
And establishing a simulation model according to the transfer function model, and creating a subsystem.
In the conventional PID structure, r (t) is a reference input signal, e (t) is a control deviation signal, u (t) is a control signal, and y (t) is a controlled system output signal. Wherein the control deviation signal e (t) =r (t) -y (t), the control signal u (t) is:
Wherein K p is a proportionality coefficient, T i is an integration time constant, and T d is a differentiation time constant; integral coefficient The differential coefficient K d＝Kp·Td.
FIG. 4 is a schematic diagram of the fuzzy PID control system according to the present invention. Δk p、ΔKi、ΔKd in the figure is the setting value of K p、Ki、Kd. The rotational speed deviation e and the rotational speed deviation change rate ec are set by a fuzzy controller to obtain a correction value K p、Ki、Kd. The input e and ec domains are [ -3,3], and the output ΔK p、ΔKi、ΔKd domains are [ -3,3]. Before the blurring process, the e and ec basic domains are mapped to the corresponding fuzzy theory domains through quantization factors. ΔK p、ΔKi、ΔKd also needs to be mapped to the corresponding domain via a scale factor. Let K p、Ki、Kd be the setting value K' p、K′i、K′d. And carrying out fuzzy reasoning by using a fuzzy rule to obtain a setting value delta K p、ΔKi、ΔKd of K p、Ki、Kd. Obtaining PID parameters through a parameter calculation formula (7):
The quantization factor in the invention is K e、Kec, and the scale factor is K a、Kb、Kc. e. ec sets the subsets to: { Negative Big (NB), negative Medium (NM), negative Small (NS), zero (ZO), positive Small (PS), median (PM), positive Big (PB) }, ΔK p、ΔKi、ΔKd sets the subset to { (big) B, (medium) M, (small) S }. Both input and output are set to trimf (triangle membership function). The control rules are set and expressed in if-then form as shown in tables 1-3. And the DeltaK p、ΔKi、ΔKd output by the fuzzy controller adjusts the proportion parameter Kp, the integral parameter Ki and the differential parameter Kd of the PID through a multiplication operator x and an adder, and the output signal is sent to a system model module.
TABLE 1 DeltaKp
TABLE 2 DeltaKi
TABLE 3 DeltaKd
FIG. 5 is a block diagram of an improved particle swarm optimization fuzzy PID controller according to the invention. The scaling factor K a、Kb、Kc and the quantization factor K e、Kec in the fuzzy PID controller are optimized by the improved particle swarm algorithm, and the optimal value is sent into the controller.
The specific parameters inside the modified PSO master function are as follows:
The particle number is 50; the maximum iteration number is 50; the dimension of the particle motion space is 5, and the dimension corresponds to a scale factor K a、Kb、Kc and a quantization factor K e、Kec in the fuzzy PID controller respectively; the initial value of the inertia weight factor omega is set to be 1; acceleration factors c1 and c2 of PSO are each set to 20; the range of the scale factor K a、Kb、Kc is set to [ -8, 15] according to the empirical quantization factor K e、Kec, 5.
The inertia weight factor in the traditional PSO algorithm speed updating formula is constant, under the condition of more variables, the solving result often does not accord with reality, and the phenomena of local convergence, slow convergence speed and poor precision exist, so the invention provides an improved particle swarm optimization algorithm with the inertia weight attenuated by the power finger function number; the invention adopts the strategy of inertia weight power exponential decay, the step length in the initial stage is large, a wider search area is generated, and local convergence is avoided; the later stage step length change is small, the speed update is small, the optimizing precision is improved, the later iteration step length is small, the oscillation around the optimal solution can be avoided, and the stability is better. The invention obtains the ITAE fitness function value (formula 8) by the particle bringing system, and the smaller the fitness value is, the better the system performance is. The invention can find the particle carrying model with the minimum adaptation value before the maximum iteration number by improving the algorithm, that is, the jump-out loop only needs to judge whether the maximum iteration number is reached or not, and the model can be replaced by other DEH models, so that the applicability is stronger.
In the improved PSO algorithm, the fitness function is J, the speed updating formula v i (k+1), the inertia weight factor omega formula and the position updating formula x i (k+1) are respectively shown in formulas (8) to (11):
vi(k+1)＝vi(k)+c1r1i(pi(k)-xi(k))+c2r2i(pg(k)-xi(k)) (9)
ω＝αE-βE (10)
xi(k+1)＝xi(k)+vi(k+1) (11)
Wherein e (t) is a control deviation signal, c 1 and c 2 are acceleration factors for improving PSO respectively, and are constants, r 1i and r 2i are random numbers of a (0, 1) range of the particle i in the iterative process, and r 1i and r 2i are re-valued in each iteration, so that the movement speed of each particle in each iteration is different; i=1, 2,3,4,5; v i (k) and v i (k+1) are the velocities of particle i at times k and k+1, respectively; x i (k) and x i (k+1) are the positions of particle i at times k and k+1, respectively; p i (k) is the optimum position searched so far for particle i; p g (k) is the optimum position searched so far for the whole population of particles; omega is an improved PSO inertial weight factor; alpha E -βE is a power exponent function, E is the iteration number, and alpha and beta are positive numbers.
To verify the effectiveness of the algorithm of the present invention (hereinafter referred to as P-EXPPSO), three reference functions, sphere function (Sphere), banana function (Rosenbrock) and worker bee function (RASTRIGIN), were used to test the algorithm performance and compared with the LDWPSO algorithm with linear change of weight factor and the CFPSO algorithm with compression factor. The number of particles was 50 and the iterations were 30. The operation results are shown in fig. 6-8, and fig. 6 is a test comparison diagram of the improved PSO algorithm P-EXPPSO and the LDWPSO and CFPSO in Sphere functions; FIG. 7 is a graph comparing the improved PSO algorithm P-EXPPSO of the present invention with the tests of LDWPSO, CFPSO in Rosenbrock function; FIG. 8 is a graph comparing the improved PSO algorithm P-EXPPSO of the present invention with the tests of LDWPSO, CFPSO in RASTRIGIN function; it can be seen that under the test of three reference functions of Sphere curved surface function (Sphere), banana function (Rosenbrock) and worker bee function (RASTRIGIN), the P-EXPPSO algorithm of the invention has faster convergence speed and accurate optimizing result.
The invention adopts a particle swarm optimization algorithm to optimize fuzzy PID parameters, takes the error of a system as the input of an adaptation function which is the evaluation function of the particle swarm optimization algorithm, calculates the numerical value of the adaptation function, then adjusts 5 parameters of a proportion factor K a、Kb、Kc and a quantization factor K e、Kec of the fuzzy PID controller according to the adaptation degree of the function, and searches an optimal value in a parameter space of a variable, so that the control performance of the system achieves the best effect.
FIG. 9 is an overall simulation flow chart of the present invention. And (3) running an m file containing an improved particle swarm algorithm, generating a particle swarm to assign values for a quantization factor and a scale factor, running a simulink simulation, outputting a performance index, namely an adaptive function value, and finally returning to a program to update the particle swarm to finish one iteration. After the condition of 50 times of maximum iteration is met, the program automatically stops running, the optimal solution is imported into a working space, a comparison waveform diagram is drawn, and the data are analyzed.
Fig. 10 and fig. 11 are respectively quantization factor (K e、Kec) and scaling factor (K a、Kb、Kc) iteration curves, and fig. 12 is an adaptive value function J iteration curve, and it can be seen that the curves tend to be stable around 16 iterations, and convergence is quicker, wherein K e＝15.61,Kec＝4.76,Ka＝1.24,Kb＝0.93,Kc =1.35; j=35211.
FIG. 13 is a graph showing the comparison of the output waveforms of three control strategies, namely a closed-loop conventional PID control system (PID), a closed-loop Fuzzy PID control system (Fuzzy-PID) and a control system (P-EXPPSO Fuzzy-PID) for optimizing Fuzzy PID parameters by improving the PSO algorithm. Table 4 shows specific control performance data.
TABLE 4 DEH control Performance index under different strategies
As can be seen from the graph 13 and the graph 4, compared with the fuzzy PID control and the traditional PID control method, the control method for optimizing the fuzzy PID parameters by adopting the improved PSO algorithm for the rotational speed control of the DEH system of the thermal power generating unit has the advantages that the adjustment time is respectively shortened by 0.3s and 3.04s, the overshoot is reduced by 8.33 percent and 25.81 percent, and the adaptation value is reduced by 3093.86 and 10309.42; the waveform oscillation is smaller, and the method has the advantages of better robustness and the like.
The foregoing is only a preferred embodiment of the invention, it being noted that: it will be apparent to those skilled in the art that various modifications and adaptations can be made without departing from the principles of the present invention, and such modifications and adaptations are intended to be comprehended within the scope of the invention.
Claims (4)
1. The thermal power unit DEH rotating speed control method based on improved PSO-fuzzy PID is characterized by comprising the following steps of:
s1, constructing a thermal power unit DEH system: constructing a DEH system of the thermal power unit, which mainly comprises an electrohydraulic converter, a steam turbine, an oil motor, a transmission mechanism, a steam volume and a reheating volume;
s2, establishing a fuzzy PID control module: the method comprises the steps of constructing a fuzzy PID control module, wherein the fuzzy controller is used for adjusting control parameters of the fuzzy PID control module, an input signal is a rotating speed deviation and a rotating speed deviation change rate, and an output signal is an adjusting variable of the PID controller;
S3, establishing an improved PSO-fuzzy PID control module: introducing an improved PSO algorithm to optimize a fuzzy PID control module, adopting the improved PSO algorithm to optimize a scale factor and a quantization factor in the fuzzy PID control module, sending an optimal value into the fuzzy PID control module, adopting the improved PSO algorithm to take an error signal of a DEH system of the thermal power generating unit as an adaptability function, and setting inertia weight of power exponential decay;
S4, controlling the rotating speed of a DEH system of the thermal power generating unit: the improved PSO-fuzzy PID control module in the step S3 is applied to the DEH system of the thermal power unit in the step S1 to form closed loop feedback, so that the rotation speed control of the DEH system of the thermal power unit is realized;
the calculation formula of the inertial weight of the power exponential decay is as follows:
ω＝αE-βE
wherein ω is an improved PSO inertial weight factor; alpha E -βE is a power exponent function, E is the iteration number, and alpha and beta are positive numbers.
2. The thermal power generating unit DEH rotating speed control method based on improved PSO-fuzzy PID of claim 1, wherein the method comprises the following steps: the calculation formula of the fitness function is as follows:
wherein J is a fitness function, e (t) is a control deviation signal, namely an error signal of a DEH system of the thermal power unit, and t is a time.
3. The thermal power generating unit DEH rotating speed control method based on improved PSO-fuzzy PID of claim 1, wherein the method comprises the following steps: in the fuzzy controller in the step S2, the input signal is the rotational speed deviation e and the rotational speed deviation change rate ec, the output signal is the adjustment variable K p、Ki、Kd of the PID controller, the quantization factor is K e、Kec, and the scale factor is K a、Kb、Kc; the rotational speed deviation e and the rotational speed deviation change rate ec set the subsets as: { Negative Big (NB), negative Medium (NM), negative Small (NS), zero (ZO), positive Small (PS), median (PM), positive Big (PB) }, ΔK p、ΔKo、ΔKd sets the subset to { B, M, S }; the input and the output are set to trimf; control rules are set and the rule table is expressed in the form of if-then.
4. The thermal power generating unit DEH rotating speed control method based on improved PSO-fuzzy PID of claim 1, wherein the method comprises the following steps: the mathematical model of the DEH system of the thermal power generating unit in the step S1 is as follows:
Wherein G 1(S)、G2(S)、G3(S)、G4 (S) is a transfer function model of an electrohydraulic converter, an oil engine, a steam volume, and a reheat volume, T y is an electrohydraulic converter time constant, T c is an oil engine time constant, T CH is a high pressure steam chamber steam volume time constant, T RH is a reheat steam volume time constant, and T 0 =1.5 is a delay time of a reheat system.
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