CN115356916A - Parameter optimization method for PI controller of subcritical thermal power generating unit coordination system - Google Patents
Parameter optimization method for PI controller of subcritical thermal power generating unit coordination system Download PDFInfo
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Abstract
The invention discloses a parameter optimization method for a PI controller of a subcritical thermal power generating unit coordination system. According to the method, on the basis of a particle swarm algorithm, the value taking mode of the learning weight of the individual particles is improved, the convergence speed of the optimization process is effectively improved, and the probability that the optimization result falls into a local optimal point is reduced. And then, optimizing by using an improved particle swarm algorithm to obtain optimal parameters of a steam turbine main control and a boiler main control PI controller of the subcritical thermal power generating unit coordination system, and optimizing the performance of the control system. The method can overcome the adverse effects on the control caused by the nonlinearity and the time-varying property of the unit to a certain extent, effectively ensure the control effect of the subcritical thermal power unit when the load is varied rapidly in a large range, and improve the safety and the stability of the unit operation.
Description
Technical Field
The invention relates to a thermal power generating unit coordination system PI controller parameter optimization method based on an improved particle swarm optimization algorithm, and belongs to the fields of thermal power engineering and automatic control.
Background
A subcritical thermal power generating unit Coordinated Control System (CCS) takes a pair of objects with large difference of speed characteristics, namely a steam turbine and a boiler, as a whole to control so as to complete the following tasks: (1) When the load instruction is not changed, the main steam pressure fluctuation and the load deviation caused by disturbance can be eliminated; (2) When the load instruction is changed, the power response can be ensured to be timely outside, and the main steam pressure deviation can be controlled to be within an allowable range inside, so that the contradiction between the pressure and the power is balanced, and the safe and stable operation of the unit is maintained. A subcritical thermal power generating unit Coordinated Control System (CCS) can be regarded as a thermal power plant center and is of great importance to safe and stable operation of the unit.
In the actual production process, in the process of changing load of a thermal power generating unit in a large range, the dynamic characteristics of a controlled object of a coordination system can change obviously, the nonlinearity of the controlled object is especially obvious, and if a fixed controller parameter is adopted, the serious mismatch between the controller parameter and the object characteristic can be caused, so that the control performance of the coordination system is degraded. At present, flexibility transformation of a thermal power generating unit is comprehensively promoted, higher requirements are provided for a subcritical thermal power generating unit coordination control system, and targeted optimization is necessarily made aiming at the problems of the subcritical thermal power generating unit coordination control system.
Disclosure of Invention
The invention aims to provide a method for optimizing parameters of a PI controller of a subcritical thermal power generating unit coordination system, and solves the problems mentioned in the background.
The technical scheme of the invention is that a subcritical thermal power generating unit coordination system PI controller parameter optimization method is characterized in that: the transfer function expression of the boiler master control PI controller is as follows:
the transfer function expression of the steam turbine main control PI controller is as follows:
in the formula, K p1 、T i1 Proportional coefficient and integral time of a boiler master control PI controller are respectively; k p2 、T i2 Proportional coefficient and integral time of a steam turbine main control PI controller are respectively;
the method comprises the following steps of determining the proportional coefficient and the integral time of a steam turbine main control PI controller and the proportional coefficient and the integral time of a PI controller in a boiler main control through an improved particle swarm algorithm to ensure the stability of a control system, wherein a fitness function of the particle swarm algorithm is constructed as follows:
in the formula, in the formula
And
respectively the machine side main steam pressure deviation and the load deviation, a 1 And a is 2 For the weight coefficient corresponding to the deviation, the PI controller parameter K p1 、T i1 、K p2 、T i2 The specific optimizing steps are as follows: initializing each parameter of the particle swarm algorithm, and calculating the current PI controller parameter K in each period p1 、T i1 、K p2 、T i2 And a corresponding fitness function J is used for further judging whether a termination condition is met or not, if so, iteration is stopped to obtain a global optimal solution, and finally a parameter K of the PI controller is obtained p1 、T i1 、K p2 、T i2 The optimization value of (1); and if the position and the speed of each particle are not met, continuously updating the position and the speed of each particle until a termination condition is met.
The improved particle swarm algorithm is as follows: in the process of searching the optimal solution in the D-dimensional space, searching the historical information of the initial-stage emphasis body and the historical information and population experience of the middle-and-later-stage emphasis particle population, thereby representing the learning weight c of individual cognition m The learning weight c should be large first and small second to characterize social experience n First, it should be small and then large, so studyExercise weight c m ，c n The following form is adopted:
wherein, t e To set the number of iterations.
The invention has the following beneficial effects:
according to the method, on the basis of a particle swarm algorithm, the value taking mode of the learning weight of the individual particles is improved, the convergence speed of the optimization process is effectively improved, and the probability that the optimization result falls into a local optimal point is reduced. And then, optimizing by using an improved particle swarm algorithm to obtain optimal parameters of a steam turbine master control and a boiler master control PI controller of the subcritical thermal power generating unit coordination system, and optimizing the performance of the control system. The method can overcome the adverse effects on the control caused by the nonlinearity and the time-varying property of the unit to a certain degree, effectively ensure the control effect of the subcritical thermal power unit during the rapid large-range load variation, and improve the safety and the stability of the unit operation.
Drawings
Fig. 1 is a schematic structural diagram of a coordinated control system of a subcritical thermal power generating unit.
Detailed Description
As shown in fig. 1, a specific implementation method of the parameter optimization method for the PI controller of the thermal power generating unit coordination system based on the improved particle swarm optimization is as follows:
the particle swarm algorithm independently searches an optimal solution in a search space by utilizing a plurality of particles without mass, wherein the optimal solution is the particle position corresponding to the minimum value of a fitness function, a particle swarm consisting of s particles is supposed to exist in a D-dimensional space, and the position x of the ith particle in the swarm i And velocity v i As shown in formula (1):
in the formula, x ij A j-th dimension component which is a position of the i-th particle in the D-dimension space; v. of ij Is the ith particle at DThe j-th dimension component of the velocity in dimensional space.
The update mechanism of the ith particle position and speed is shown in formula (2) and formula (3):
wherein the content of the first and second substances,
and
the j-dimension component of the position of the ith particle in the D-dimension space at the time t and the time t +1 respectively;
and
the j-th dimension component of the velocity of the ith particle in the D-dimension space at the t moment and the t-1 moment;
a j-dimension component of the ith particle history optimal position in the D-dimension space at the time of t-1;
a j-dimension component of the optimal position of the particle population in the D-dimension space at the time of t-1; omega is an inertia weight and represents a coefficient of the original speed degree of the particles; c. C m ，c n Represents a learning weight, c m Weight coefficient being history information of particle-weighted ontology, c n Weighting factor, r, of historical information of particle-weighted particle populations m ，r n Is [0,1 ]]Random numbers within a range.
Single particle searching process in D-dimensional space for optimal solutionSearching the history information of the initial stage of emphasizing the ontology and searching the history information and population experience of the middle and later stages of emphasizing the particle population, thereby representing the learning weight c of individual cognition m The learning weight c of social experience should be characterized first large and then small n Should be small first and then large, then the learning weight c m ，c n The following form is adopted:
wherein, t e To set the number of iterations.
The controlled object of the subcritical coal-fired unit coordination system can be regarded as a 2 x 2 multivariable object, and the input of the object is u 1 、u 2 Respectively representing a combustion quantity command and a steam turbine valve opening command, and the output of the system is y 1 、y 2 The main steam pressure of the machine side and the actual generating power of the machine set are respectively represented, and the input and output relationship can be described by the following formula:
in the formula G 11 (s)、G 21 (s) are the command u for combustion amount, respectively 1 To input, y 1 、y 2 For output transfer function model (steam turbine regulating valve opening command u) 2 Remain unchanged); g 12 (s)、G 22 (s) are respectively the opening command u of the steam turbine regulating valve 2 To input, y 1 、y 2 Transfer function model for output (combustion quantity command u) 1 Remain unchanged).
A PI (proportional integral) controller is adopted as a basic form of the steam turbine master control and the boiler master control of a subcritical coal-fired unit coordination system, the control strategy is shown in figure 1, and y in the figure 1r 、y 2r Respectively representing a set value of the main steam pressure of the machine side and a load instruction of the unit. The transfer function expression of the boiler master control PI controller is as follows:
the transfer function expression of the steam turbine main control PI controller is as follows:
in the formula, K p1 、T i1 Proportional coefficient and integral time of a boiler master control PI controller are respectively; k is p2 、T i2 The proportional coefficient and the integral time of the steam turbine main control PI controller are respectively.
The method comprises the following steps of determining the proportional coefficient and the integral time of PI controllers in the steam turbine main control and the boiler main control through an improved particle swarm algorithm to ensure the stability of a control system, wherein the fitness function of the particle swarm algorithm is constructed as follows:
in the formula, in the formula
And
respectively a main engine side steam pressure deviation and a load deviation a 1 And a is 2 The weight coefficient corresponding to the deviation. PI controller parameter K p1 、T i1 、K p2 、T i2 The specific optimization steps are as follows: initializing each parameter of the particle swarm algorithm, and calculating the current PI controller parameter K in each period p1 、T i1 、K p2 、T i2 And a corresponding fitness function J is used for further judging whether a termination condition is met or not, if so, iteration is stopped to obtain a global optimal solution, and finally a parameter K of the PI controller is obtained p1 、T i1 、K p2 、T i2 The optimization value of (1); and if not, continuously updating the position and the speed of each particle until the termination condition is met.
Claims (1)
1. A subcritical thermal power generating unit coordination system PI controller parameter optimization method is characterized in that: the transfer function expression of the boiler master control PI controller is as follows:
the transfer function expression of the steam turbine main control PI controller is as follows:
in the formula, K p1 、T i1 Proportional coefficient and integral time of a boiler master control PI controller are respectively; k p2 、T i2 Proportional coefficient and integral time of a steam turbine main control PI controller are respectively;
the method comprises the following steps of determining the proportional coefficient and the integral time of a steam turbine main control PI controller and the proportional coefficient and the integral time of a PI controller in a boiler main control through an improved particle swarm algorithm to ensure the stability of a control system, wherein a fitness function of the particle swarm algorithm is constructed as follows:
in the formula (II)
And
respectively the machine side main steam pressure deviation and the load deviation, a 1 And a is 2 For the weight coefficient corresponding to the deviation, PI controller parameter K p1 、T i1 、K p2 、T i2 The specific optimizing steps are as follows: initializing each parameter of the particle swarm algorithm, and calculating the current PI controller parameter K in each period p1 、T i1 、K p2 、T i2 And a corresponding fitness function J is used for further judging whether a termination condition is met or not, if so, iteration is stopped to obtain a global optimal solution, and finally a parameter K of the PI controller is obtained p1 、T i1 、K p2 、T i2 The optimization value of (2); if not, continuously updating the positions and the speeds of the particles until the termination condition is met;
the improved particle swarm algorithm is as follows: in the process of searching the optimal solution in the D-dimensional space, searching the historical information of the initial-stage emphasis body and the historical information and population experience of the middle-and-later-stage emphasis particle population, thereby representing the learning weight c of individual cognition m The learning weight c should be large first and small second to characterize social experience n Should be small first and then large, then the learning weight c m ，c n The following form is adopted:
wherein, t e To set the number of iterations.
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