CN114167717A - Thermal power generating unit DEH rotating speed control method based on improved PSO-fuzzy PID - Google Patents
Thermal power generating 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 generating 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, optimizing a proportional factor and a quantization factor in the fuzzy PID control module by adopting the improved PSO algorithm in the fuzzy PID control module, sending an optimal value into the fuzzy PID control module, taking an error signal of a DEH system of the thermal power generating unit as a fitness function by the improved PSO algorithm, and setting an inertia weight of power exponential decay; the improved PSO-fuzzy PID control module is applied to a thermal power unit DEH system to form closed-loop feedback, and rotation speed control of the thermal power unit DEH system is achieved. 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 inertia weight of power exponent attenuation is set, so that the method has the advantages of fast convergence, good stability of system control, fast response time, good robustness of the system 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 steam turbine plays an important role in the power generation process of the thermal power generating unit, and is a rotary steam power device. The DEH rotating 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 the PID, so that the result error is large, and the control effect is not ideal. With the national requirements on the safety of power plants and the accuracy of the operation of nuclear power plants, the steam turbine control system of the thermal power generating unit with large installed capacity and high parameters has higher requirements. The traditional experience-based trial and error method cannot meet the requirement of more accurate control at present, has many potential safety hazards, and does not allow an operation engineer to perform frequent trial and error in the actual operation process. In view of the fact that the automation degree of the controller is not high enough, the research and development of the novel DEH controller for the thermal power generating unit has important application value.
The research on process control systems is often based on mathematical models of transfer functions, however, the actual thermal power unit system is a large complex system in which many time-varying uncertainties and nonlinearities exist, which are difficult to accurately model. In view of the advantages of no need of accurate mathematical model and strong robustness of fuzzy control, the system is controlled by the fuzzy PID controller. The fuzzy PID algorithm needs an expert to give fuzzy rules, and errors exist in the parameter adjusting process.
Disclosure of Invention
The purpose of the invention is as follows: aiming at the defect of large parameter condition error when a fuzzy PID controller is adopted to control the DEH of the thermal power generating unit in the prior art, the invention controls the rotating speed of the DEH of the thermal power generating unit by improving the PSO-fuzzy PID, takes an error signal of a DEH system of the thermal power generating unit as a fitness function in an improved PSO algorithm, sets the inertia weight of power exponential decay, and has the advantages of fast convergence, good stability of system control, fast response time, good robustness of the system 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 DEH system of the thermal power generating unit: constructing a thermal power generating unit DEH system mainly comprising an electro-hydraulic converter, a steam turbine, an oil-driven machine, a transmission mechanism, a steam volume and a reheating volume;
s2, establishing a fuzzy PID control module: constructing a fuzzy PID control module, which comprises a fuzzy controller and a PID controller, wherein the fuzzy controller is used for adjusting control parameters of the fuzzy PID control module, input signals are rotating speed deviation and rotating speed deviation change rate, and output signals are regulating variables 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, optimizing a proportional factor and a quantization factor in the fuzzy PID control module by adopting the improved PSO algorithm in the fuzzy PID control module, sending an optimal value into the fuzzy PID control module, taking an error signal of a DEH system of the thermal power generating unit as a fitness function by the improved PSO algorithm, and setting an inertia weight of power exponential decay;
s4, controlling the rotation speed of a DEH system of the thermal power generating unit: and (4) 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 that the rotation speed control of the thermal power unit DEH system is realized.
Preferably, the formula for calculating the inertia weight of the power exponential decay is as follows:
ω＝αE-βE
wherein, omega is an improved PSO inertia weight factor; alpha E-βEFor power exponential function, E is iteration number, and alpha and beta are positive numbers.
Preferably, 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 generating unit, and t is a moment.
Preferably, in the improved PSO algorithm, the spatial dimension of the particle motion is 5, and each dimension corresponds to a scale factor K in the fuzzy PID controllera、Kb、XcAnd a quantization factor Ke、Kec(ii) a The velocity update formula and the position update formula of the particle are respectively as follows:
vi(k+1)＝vi(k)+c1r1i(pi(k)-xi(k))+c2r2i(pg(k)-xi(k))
xi(k+1)＝xi(k)+vi(k+1)
wherein, c1And c2Acceleration factor, r, respectively, for improved PSO1iAnd r2iIs a random number in the range of (0, 1) for particle i in the iterative process, i is 1, 2, 3, 4, 5; v. ofi(k) And vi(k +1) is the velocity of particle i in the kth and kth +1 iterations, respectively; x is the number ofi(k) And xi(k +1) is the position of the particle i in the kth and k +1 th iterations, pi(k) The optimal position searched so far for particle i; p is a radical ofg(k) The optimal position has been searched for the entire population of particles so far.
Preferably, said r1iAnd r2iThe re-value is performed in each iteration.
Preferably, in the fuzzy controller of step S2, the input signal is the rotation speed deviation e and the rotation speed deviation change rate ec, and the output signal is the adjustment variable K of the PID controllerp、Ki、KdThe quantization factor is Ke、KecThe scale factor is Ka、Kb、Kc(ii) a The rotation speed deviation e and the rotation speed deviation change rate ec are set as follows: { negative large (NB), Negative Medium (NM), Negative Small (NS), Zero (ZO), Positive Small (PS), Positive Medium (PM), positive large (PB) }, Δ Kp、ΔKi、ΔKdThe subset is set to B, M,s }; the input and the output are both set to be trimf type; setting a control rule, and expressing the rule table in the form of if-then.
Preferably, the mathematical model of the DEH system of the thermal power generating unit in step S1 is as follows:
wherein G is1(S)、G2(S)、G3(S)、G4(S) transfer function models of the electrohydraulic converter, the servomotor, the steam volume, and the reheat volume, respectively, TyBeing time constant of electrohydraulic converters, TcIs the time constant of the servomotor, TCHIs the time constant of the steam volume in the high-pressure steam chamber, TRHIs reheat steam volume time constant, t01.5 is the delay time of the reheat system.
Has the advantages that: the method controls the rotating speed of the DEH of the thermal power generating unit by improving the PSO-fuzzy PID, takes an error signal of the DEH system of the thermal power generating unit as a fitness function in an improved PSO algorithm, sets the inertia weight of power exponential decay, 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 DEH of the thermal power generating unit.
Drawings
FIG. 1 is a flow chart of a method of the present invention;
FIG. 2 is a schematic diagram of a simulation architecture;
FIG. 3 is a schematic diagram of a simulation detailed structure of the DEH system of the thermoelectric generator set in FIG. 2;
FIG. 4 is a schematic block diagram of a fuzzy PID control module of the invention;
FIG. 5 is a functional block diagram of an improved PSO-fuzzy PID control module of the present invention;
FIG. 6 is a comparison graph of the testing of the improved PSO algorithm P-EXPPSO, LDWPSO and CFPSO in the Sphere function according to the present invention;
FIG. 7 is a comparison graph of the improved PSO algorithm P-EXPPSO of the present invention with the tests of LDWPSO and CFPSO in the Rosenbrock function;
FIG. 8 is a comparison graph of the testing of the improved PSO algorithm P-EXPPSO, LDWPSO and CFPSO in the Rastrigin function according to the present invention;
FIG. 9 is a flow chart of the operation of the improved PSO-fuzzy PID control module of the present invention;
FIG. 10 is an iterative graph of the quantization factor of the improved PSO-fuzzy PID control block of the present invention;
FIG. 11 is an iterative graph of the scale factor of the improved PSO-fuzzy PID control module of the present invention;
FIG. 12 is an iterative plot of the adaptation function values of the improved PSO-fuzzy PID control module of the present invention;
fig. 13 is an output curve diagram of the improved PSO-fuzzy PID control module in fig. 2, the conventional PID controller and the fuzzy PID controller applied to the DEH system of the thermal power generating unit.
Detailed Description
The invention further describes and explains a thermal power generating unit DEH rotating speed control method based on improved PSO-fuzzy PID with reference to the attached drawings and embodiments. Wherein, DEH: digital Electronic Hydraulic, an exponential, electro-Hydraulic regulation system.
As shown in the attached figure 1, the thermal power generating unit DEH rotating speed control method based on the improved PSO-fuzzy PID comprises the following steps:
s1, constructing a DEH system of the thermal power generating unit: constructing a thermal power generating unit DEH system mainly comprising an electro-hydraulic converter, a steam turbine, an oil-driven machine, 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 is1(S)、G2(S)、G3(S)、G4(S) transfer function models of the electrohydraulic converter, the servomotor, the steam volume, and the reheat volume, respectively, TyBeing time constant of electrohydraulic converters, TcIs the time constant of the servomotor, TCHIs the time constant of the steam volume in the high-pressure steam chamber, TRHIs reheat steam volume time constant, t01.5 is the delay time of the reheat system.
S2, establishing a fuzzy PID control module: constructing a fuzzy PID control module, which comprises a fuzzy controller and a PID controller, wherein the fuzzy controller is used for adjusting control parameters of the fuzzy PID control module, input signals are rotating speed deviation and rotating speed deviation change rate, and output signals are regulating variables of the PID controller;
in the fuzzy controller, the input signal is the rotation speed deviation e and the rotation speed deviation change rate ec, and the output signal is the regulating variable K of the PID controllerp、Ki、KdThe quantization factor is Ke、KecThe scale factor is Ka、Kb、Kc(ii) a The rotation speed deviation e and the rotation speed deviation change rate ec are set as follows: { negative large (NB), Negative Medium (NM), Negative Small (NS), Zero (ZO), Positive Small (PS), Positive Medium (PM), positive large (PB) }, Δ Kp、ΔKi、ΔKdSet the subset to { B, M, S }; both input and output are providedIs trimf type; setting a control rule, and expressing the rule table 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, optimizing a proportional factor and a quantization factor in the fuzzy PID control module by adopting the improved PSO algorithm in the fuzzy PID control module, sending an optimal value into the fuzzy PID control module, taking an error signal of a DEH system of the thermal power generating unit as a fitness function by the improved PSO algorithm, and setting an inertia weight of power exponential decay;
wherein, the calculation formula of the inertia weight of the power exponential decay is as follows:
ω＝αE-βE
wherein, omega is an improved PSO inertia weight factor; alpha E-βEFor power exponential function, E is iteration number, and alpha and beta are positive numbers.
The fitness function is calculated by the 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 generating unit, and t is a moment.
In the improved PSO algorithm, the spatial dimension of the particle motion is 5, and the spatial dimension corresponds to a scale factor K in a fuzzy PID controller respectivelya、Kb、KcAnd a quantization factor Ke、Kec(ii) a The velocity update formula and the position update formula of the particle are respectively as follows:
vi(k+1)＝vi(k)+c1r1i(pi(k)-xi(k))+c2r2i(pg(k)-xi(k))
xi(k+1)＝xi(k)+vi(k+1)
wherein, c1And c2Acceleration factor, r, respectively, for improved PSO1iAnd r2iIs a random number in the range of (0, 1) for particle i in the iterative process, i is 1, 2, 3, 4, 5; v. ofi(k) Andvi(k +1) is the velocity of particle i in the kth and kth +1 iterations, respectively; x is the number ofi(k) And xi(k +1) is the position of the particle i in the kth and k +1 th iterations, pi(k) The optimal position searched so far for particle i; p is a radical ofg(k) The optimal position has been searched for the entire population of particles so far.
S4, controlling the rotation speed of a DEH system of the thermal power generating unit: 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 that the rotation 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 inertia weight of power exponent attenuation is set, so that the method has the advantages of fast convergence, good stability of system control, fast response time, good robustness of the system and the like.
Simulation verification:
as shown in fig. 2, the simulation system comprises: 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 consists of an amplifier module, an integral 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 generating unit DEH system model module consisting of a transfer function module, an adder, a white noise module and a delay module and a subsystem are created, as shown in FIG. 3; and the oscilloscope display unit is used for signal comparison.
Referring to the bottom part of fig. 2, a slope signal output by a given slope signal unit is used as an input signal, the input signal sequentially passes through a traditional PID control module and a system model module subsystem to generate an output signal, the output signal is used as a negative feedback signal, and the negative feedback signal and the input signal form an error signal e (t) which is sent to a traditional PID controller to form a closed-loop traditional PID control system; as shown in the middle part of fig. 2, a slope signal output by a slope signal unit is used as an input signal, the input signal sequentially passes through a fuzzy PID control module and a system model module subsystem to generate an output signal, the output signal is used as a negative feedback signal, and the negative feedback signal and the input signal form an error signal e (t) which is sent to a fuzzy PID controller to form a closed-loop fuzzy PID control system; as shown in the top part of the attached figure 2, error signals e (t) of the same closed-loop fuzzy PID control system are simultaneously sent to an adaptive function module, the fuzzy PID parameters are optimized through the improved PSO algorithm, and the optimized parameters are sent to a fuzzy PID controller to form the control system for optimizing the fuzzy PID parameters through the improved PSO algorithm. And the output of the three parts of control systems is connected with a comparative oscilloscope display unit to form the whole simulation control system.
In order to facilitate the design of a controller, a DEH system of the thermal power generating unit is simplified and consists of an electro-hydraulic converter, a steam turbine, an oil-driven machine, a transmission mechanism, a steam volume and a reheating volume. Except for 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 a DEH system of a thermal power generating unit, an electro-hydraulic converter receives an electric signal and sends the electric signal to a steam turbine to generate an oil pressure difference to enable a sliding valve to displace, and a mathematical model is as follows:
wherein G is1(S) is a transfer function model of the electrohydraulic converter, TyThe value of 0.2 is the time constant of the electro-hydraulic converter (usually 0.01-0.9).
The slide valve displacement drives the piston of the oil-operated engine to move, and the adjusting air valve is driven by the transmission mechanism. The mathematical model is as in formula (2):
wherein S ishFor slide valve displacement, TcAnd sigma is the variation of the servomotor motion. The transfer function is of the form (3):
wherein G is2(S) is a transfer function model of the servomotor, TcThe value of 0.3 can be adjusted according to requirements.
The steam flow is adjusted by an air valve and expanded to do work through a high-pressure cylinder and a middle reheating pipe. The mathematical model is as in equation (4-6).
Wherein G is3(S) is a transfer function model of the vapor volume, TCHThe value of 0.35 is the volume time constant of the high-pressure steam chamber (usually 0.3-0.5).
Wherein G is4(S) is a model of the transfer function of the reheat volume, T RH8 is reheat steam volume time constant, t0The reheat volume delay time is 1.5.
A white noise generator and an adder are added to serve as interference signals of steam pressure disturbance, and sampling time is set to be 0.1.
And establishing a simulation model according to the transfer function model and establishing a subsystem.
In the conventional PID structure, r (t) is a reference input signal, e (t) is a control offset 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)), and the control signal u (t) is:
wherein KpIs a proportionality coefficient, TiTo integrate the time constant, TdIs a differential time constant; integral coefficient
Differential coefficient Kd＝Kp·Td。
FIG. 4 is a schematic diagram of the fuzzy PID control system of the present invention. In the figure,. DELTA.Kp、ΔKi、ΔKdIs Kp、Ki、KdThe setting value of (1). The rotating speed deviation e and the rotating speed deviation change rate ec are set by a fuzzy controller to obtain a corrected value Kp、Ki、Kd. The input quantity e and ec have a discourse field of [ -3, 3]Output quantity Δ Kp、ΔKi、ΔKdDiscourse domain is [ -3, 3]. Before fuzzy processing, e and ec basic discourse domains need to be mapped to corresponding fuzzy set discourse domains through quantization factors. Δ Kp、ΔKi、ΔKdAnd also mapped to the domain of interest via the scale factor. Let Kp、Ki、KdIs K 'as a value to be set'p、K′i、K′d. Fuzzy inference is carried out by using fuzzy rule to obtain Kp、Ki、KdSetting value ofp、ΔKi、ΔKd. Obtaining PID parameters through a parameter calculation formula (7):
in the present invention, the quantization factor is Ke、KecThe scale factor is Ka、Kb、Kc. e. ec sets the subsets to each: { negative large (NB), Negative Medium (NM), Negative Small (NS), Zero (ZO), Positive Small (PS), Positive Medium (PM), positive large (PB) }, Δ Kp、ΔKi、ΔKdThe subset is set to { (large) B, (medium) M, (small) S }. Both the input and output are set to trimf type (triangular membership functions). Control rules are set and expressed in the form of if-then as shown in tables 1-3. Delta K of output of fuzzy controllerp、ΔKi、ΔKdPID proportional parameter Kp, integral parameter Ki and differential parameter Kd are set through a multiplication arithmetic unit x and an adder, and output signals are sent to a system model module.
TABLE 1 Δ Kp
TABLE 2 Δ Ki
TABLE 3. DELTA. Kd
FIG. 5 is a structural diagram of an improved particle swarm optimization fuzzy PID controller in the invention. Scaling factor K in fuzzy PID controller by improved particle swarm optimizationa、Kb、KcAnd a quantization factor Ke、KecOptimizing and sending the optimal value to the controller.
Specific parameters in the improved PSO master function are as follows:
the number of particles is 50; the maximum number of iterations is 50; the spatial dimension of the particle motion is 5, which respectively corresponds to a scale factor K in the fuzzy PID controllera、Kb、KcAnd a quantization factor Ke、Kec(ii) a Setting an initial value of an inertia weight factor omega as 1; acceleration factors c1 and c2 for the PSO are both set to 20; empirically quantifying factor Ke、KecThe range is set to [ -5, 30 [)]Scale factor Ka、Kb、KcThe range is set to [ -8, 15 [)]。
The inertial weight factor in the traditional PSO algorithm speed updating formula is constant, under the condition of more variables, the solving result is not practical, and the phenomena of local convergence, low convergence speed and poor precision exist, so the invention provides the improved particle swarm optimization algorithm with the inertial weight attenuated by the exponential function number; the invention adopts the strategy of inertia weight power exponent attenuation, the initial stage is large in step size, a wider search area is generated, and local convergence is avoided; the step change of the later stage is small, the speed updating is small, the optimizing precision is improved, the later iteration step is small, oscillation around the optimal solution can be avoided, and the stability is good. The invention obtains the ITAE fitness function value (formula 8) through the particle substitution system, and the smaller the fitness value is, the better the system performance is. By improving the advantages of the algorithm, the particle carry-in model with the minimum adaptive value can be found before the maximum iteration number, namely, the loop jump is only required to judge whether the maximum iteration number is reached or not, and other DEH models can be used instead, so that the method has strong applicability.
In the invention, the fitness function in the improved PSO algorithm is J, and the speed updating formula vi(k +1), inertia weight factor omega formula, position update formula xi(k +1) are respectively formulae (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, c1And c2Acceleration factor of improved PSO, constant, r1iAnd r2iIs a random number of particles i in the range of (0, 1) during an iteration, and r in each iteration1iAnd r2iThe values are re-taken, so that the movement speed of each particle is different in each iteration; 1, 2, 3, 4, 5; v. ofi(k) And vi(k +1) is the velocity of particle i at times k and k +1, respectively; x is the number ofi(k) And xi(k +1) is the position of particle i at times k and k +1, respectively; p is a radical ofi(k) The optimal position searched so far for particle i; p is a radical ofg(k) The optimal position searched so far for the whole particle swarm; omega is an improved PSO inertia weight factor; alpha E-βEFor power exponential function, E is iteration number, and alpha and beta are positive numbers.
In order to verify the effective components of the algorithm (hereinafter referred to as P-EXPPSO) of the invention, three reference functions, namely a spherical surface function (Sphere), a banana function (Rosenbrock) and a worker bee function (Rastrigin function), are used for testing the performance of the algorithm, and the algorithm is compared with an LDWPSO algorithm with a linearly changing weight factor and a CFPSO algorithm with a 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 graph of the improved PSO algorithm P-EXPPSO, LDWPSO and CFPSO in the Sphere function according to the present invention; FIG. 7 is a comparison graph of the improved PSO algorithm P-EXPPSO of the present invention with the tests of LDWPSO and CFPSO in the Rosenbrock function; FIG. 8 is a comparison graph of the testing of the improved PSO algorithm P-EXPPSO, LDWPSO and CFPSO in the Rastrigin function according to the present invention; it can be seen that under the test of three reference functions, namely a spherical surface function (Sphere), a banana function (Rosenbrock) and a worker bee function (Rastrigin function), the P-EXPPSO algorithm has higher convergence rate and accurate optimization result.
In the invention, the fuzzy PID parameters are optimized by adopting a particle swarm optimization algorithm, the error of a system is used as the input of an evaluation function, namely an adaptive function, of the particle swarm optimization algorithm, the numerical value of the adaptive function is calculated, and then the scale factor K of the fuzzy PID controller is adjusted according to the fitness of the functiona、Kb、KcAnd a quantization factor Ke、KecThe 5 parameters find the optimal value in the parameter space of the variables, so that the control performance of the system achieves the best effect.
FIG. 9 is a flowchart of the overall simulation of the present invention. And (3) operating an m file containing an improved particle swarm algorithm, generating a particle swarm for assigning values of the quantization factor and the scale factor, operating a simulink simulation, outputting a performance index, namely an adaptive function value, finally returning to the program, updating the particle swarm, and finishing one iteration. After the condition of maximum iteration of 50 times is met, the program automatically stops running, the optimal solution is led into a working space, a comparison oscillogram of the optimal solution is drawn, and the data are analyzed.
FIG. 10 and FIG. 11 show the quantization factor (K) respectivelye、Kec) Scale factor (K)a、Kb、Kc) An iteration curve, fig. 12 is an iteration curve of an adaptive value function J, and it can be seen that the curve tends to be stable and converges rapidly around 16 iterations, where K ise＝15.61，Kec＝4.76，Ka＝1.24，Kb＝0.93，Kc＝1.35；J＝35211。
FIG. 13 is a comparison graph of output waveforms of three control strategies of a closed-loop traditional 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 a PSO algorithm of the invention. Table 4 shows specific control performance data.
TABLE 4 DEH control Performance index under different strategies
As can be seen from fig. 13 and table 4, compared with the fuzzy PID control and the traditional PID control method, the control method for optimizing the fuzzy PID parameters by using the improved PSO algorithm for controlling the rotation speed of the DEH system of the thermal power generating unit shortens the adjustment time by 0.3s and 3.04s, reduces the overshoot by 8.33% and 25.81%, and reduces the adaptation values by 3093.86 and 10309.42; the waveform oscillation is small, and the method has the advantages of good robustness and the like.
The above description is only of the preferred embodiments of the present invention, and it should be 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 invention and these are intended to be within the scope of the invention.
Claims (7)
1. A thermal power generating unit DEH rotating speed control method based on improved PSO-fuzzy PID is characterized by comprising the following steps:
s1, constructing a DEH system of the thermal power generating unit: constructing a thermal power generating unit DEH system mainly comprising an electro-hydraulic converter, a steam turbine, an oil-driven machine, a transmission mechanism, a steam volume and a reheating volume;
s2, establishing a fuzzy PID control module: constructing a fuzzy PID control module, which comprises a fuzzy controller and a PID controller, wherein the fuzzy controller is used for adjusting control parameters of the fuzzy PID control module, input signals are rotating speed deviation and rotating speed deviation change rate, and output signals are regulating variables 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, optimizing a proportional factor and a quantization factor in the fuzzy PID control module by adopting the improved PSO algorithm in the fuzzy PID control module, sending an optimal value into the fuzzy PID control module, taking an error signal of a DEH system of the thermal power generating unit as a fitness function by the improved PSO algorithm, and setting an inertia weight of power exponential decay;
s4, controlling the rotation speed of a DEH system of the thermal power generating unit: and (4) 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 that the rotation speed control of the thermal power unit DEH system is realized.
2. The thermal power generating unit DEH rotating speed control method based on the improved PSO-fuzzy PID as claimed in claim 1, characterized in that: the calculation formula of the inertia weight of the power exponential decay is as follows:
ω＝αE-βE
wherein, omega is an improved PSO inertia weight factor; alpha E-βEFor power exponential function, E is iteration number, and alpha and beta are positive numbers.
3. The thermal power generating unit DEH rotating speed control method based on the improved PSO-fuzzy PID as claimed in claim 1, characterized in that: 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 generating unit, and t is a moment.
4. The thermal power generating unit DEH rotating speed control method based on the improved PSO-fuzzy PID as claimed in claim 1, characterized in that: in the improved PSO algorithm, the spatial dimension of the particle motion is 5, and the spatial dimension corresponds to a scale factor K in a fuzzy PID controller respectivelya、Kb、KcAnd a quantization factor Ke、Kec(ii) a The velocity update formula and the position update formula of the particle are respectively as follows:
vi(k+1)＝vi(k)+c1r1i(pi(k)-xi(k))+c2r2i(pg(k)-xi(k))
xi(k+1)＝xi(k)+vi(k+1)
wherein, c1And c2Acceleration factor, r, respectively, for improved PSO1iAnd r2iIs a random number in the range of (0, 1) for particle i in the iterative process, i is 1, 2, 3, 4, 5; v. ofi(k) And vi(k +1) is the velocity of particle i in the kth and kth +1 iterations, respectively; x is the number ofi(k) And xi(k +1) is the position of the particle i in the kth and k +1 th iterations, pi(k) The optimal position searched so far for particle i; p is a radical ofg(k) The optimal position has been searched for the entire population of particles so far.
5. The thermal power generating unit DEH rotating speed control method based on the improved PSO-fuzzy PID is characterized in that: said r1iAnd r2iThe re-value is performed in each iteration.
6. The thermal power generating unit DEH rotating speed control method based on the improved PSO-fuzzy PID as claimed in claim 1, characterized in that: the steps areIn the fuzzy controller of step S2, the input signal is the rotational speed deviation e and the rotational speed deviation change rate ec, and the output signal is the manipulated variable K of the PID controllerp、Ki、KdThe quantization factor is Ke、KecThe scale factor is Ka、Kb、Kc(ii) a The rotation speed deviation e and the rotation speed deviation change rate ec are set as follows: { negative large (NB), Negative Medium (NM), Negative Small (NS), Zero (ZO), Positive Small (PS), Positive Medium (PM), positive large (PB) }, Δ Kp、ΔKi、ΔKdSet the subset to { B, M, S }; the input and the output are both set to be trimf type; setting a control rule, and expressing the rule table in the form of if-then.
7. The thermal power generating unit DEH rotating speed control method based on the improved PSO-fuzzy PID as claimed in claim 1, characterized in that: the mathematical model of the DEH system of the thermal power generating unit in step S1 is as follows:
wherein G is1(S)、G2(S)、G3(S)、G4(S) transfer function models of the electrohydraulic converter, the servomotor, the steam volume, and the reheat volume, respectively, TyBeing time constant of electrohydraulic converters, TcIs the time constant of the servomotor, TCHIs the time of the steam volume of the high-pressure steam chamberNumber, TRHIs reheat steam volume time constant, t01.5 is the delay time of the reheat system.
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