CN115327890B - Method for optimizing main steam pressure of PID control thermal power depth peak shaving unit by improved crowd searching algorithm - Google Patents
Method for optimizing main steam pressure of PID control thermal power depth peak shaving unit by improved crowd searching algorithm Download PDFInfo
- Publication number
- CN115327890B CN115327890B CN202211119376.5A CN202211119376A CN115327890B CN 115327890 B CN115327890 B CN 115327890B CN 202211119376 A CN202211119376 A CN 202211119376A CN 115327890 B CN115327890 B CN 115327890B
- Authority
- CN
- China
- Prior art keywords
- individual
- algorithm
- searching
- steam pressure
- main steam
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
- 238000000034 method Methods 0.000 title claims abstract description 41
- 230000008569 process Effects 0.000 claims abstract description 20
- 238000009826 distribution Methods 0.000 claims abstract description 19
- 230000007246 mechanism Effects 0.000 claims abstract description 5
- 230000006870 function Effects 0.000 claims description 69
- 238000005457 optimization Methods 0.000 claims description 19
- 230000006399 behavior Effects 0.000 claims description 18
- 238000012546 transfer Methods 0.000 claims description 18
- 239000000446 fuel Substances 0.000 claims description 15
- 230000004044 response Effects 0.000 claims description 10
- 238000012360 testing method Methods 0.000 claims description 9
- 239000013598 vector Substances 0.000 claims description 9
- 230000003044 adaptive effect Effects 0.000 claims description 6
- 230000008859 change Effects 0.000 claims description 6
- 230000035772 mutation Effects 0.000 claims description 6
- 238000010845 search algorithm Methods 0.000 claims description 6
- 230000006872 improvement Effects 0.000 claims description 4
- 241000764238 Isis Species 0.000 claims description 3
- 230000009471 action Effects 0.000 claims description 3
- 238000010276 construction Methods 0.000 claims description 3
- 230000003247 decreasing effect Effects 0.000 claims description 3
- 238000005070 sampling Methods 0.000 claims description 3
- 238000004088 simulation Methods 0.000 claims description 3
- 230000000694 effects Effects 0.000 claims description 2
- 241001081179 Litsea Species 0.000 claims 1
- 235000012854 Litsea cubeba Nutrition 0.000 claims 1
- 239000011664 nicotinic acid Substances 0.000 claims 1
- 230000003449 preventive effect Effects 0.000 claims 1
- 238000012938 design process Methods 0.000 abstract description 2
- 238000004519 manufacturing process Methods 0.000 abstract description 2
- 230000001105 regulatory effect Effects 0.000 description 7
- 239000003245 coal Substances 0.000 description 2
- 238000013461 design Methods 0.000 description 2
- 238000011161 development Methods 0.000 description 2
- 238000010298 pulverizing process Methods 0.000 description 2
- 230000003068 static effect Effects 0.000 description 2
- 238000013459 approach Methods 0.000 description 1
- 230000009194 climbing Effects 0.000 description 1
- 238000002485 combustion reaction Methods 0.000 description 1
- 239000002131 composite material Substances 0.000 description 1
- 230000001276 controlling effect Effects 0.000 description 1
- 238000005520 cutting process Methods 0.000 description 1
- 238000010586 diagram Methods 0.000 description 1
- 230000008030 elimination Effects 0.000 description 1
- 238000003379 elimination reaction Methods 0.000 description 1
- 238000005265 energy consumption Methods 0.000 description 1
- 238000012613 in situ experiment Methods 0.000 description 1
- 238000012804 iterative process Methods 0.000 description 1
- 238000005065 mining Methods 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 239000002245 particle Substances 0.000 description 1
- 238000012545 processing Methods 0.000 description 1
- 238000010998 test method Methods 0.000 description 1
Classifications
-
- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B11/00—Automatic controllers
- G05B11/01—Automatic controllers electric
- G05B11/36—Automatic controllers electric with provision for obtaining particular characteristics, e.g. proportional, integral, differential
- G05B11/42—Automatic controllers electric with provision for obtaining particular characteristics, e.g. proportional, integral, differential for obtaining a characteristic which is both proportional and time-dependent, e.g. P. I., P. I. D.
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02P—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN THE PRODUCTION OR PROCESSING OF GOODS
- Y02P90/00—Enabling technologies with a potential contribution to greenhouse gas [GHG] emissions mitigation
- Y02P90/02—Total factory control, e.g. smart factories, flexible manufacturing systems [FMS] or integrated manufacturing systems [IMS]
Landscapes
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Engineering & Computer Science (AREA)
- Automation & Control Theory (AREA)
- Feedback Control In General (AREA)
Abstract
The invention provides a method for optimizing the main steam pressure of a PID control thermal power depth peak shaver set by an improved crowd searching algorithm, which is characterized in that a fuzzy step length is used as a guiding position updating mode to easily induce the algorithm to sink into local optimum, crowd lack of information exchange is easy to reduce crowd diversity and the like, and an original crowd searching algorithm is jointly improved by adopting a reverse differential evolution mechanism and a self-adaptive t distribution strategy, so that the capability of deviating from the local optimum is further enhanced. The improved SOA algorithm adopted by the invention has definite meaning and simple parameter setting in the design process of adaptability function, optimizing step length and direction, iterative updating of individual position and the like in the process of setting PID parameters, and the invention can directly model by adopting on-site actual data, so that the designed PID controller has stronger pertinence to the production process and engineering practical value.
Description
Technical Field
The invention belongs to the technical field of thermal control of thermal power units, and particularly relates to a method for optimizing main steam pressure of a PID control thermal power depth peak shaver unit by an improved crowd searching algorithm.
Background
In order to improve the new energy consumption rate and the system adjustment flexibility, the thermal power generating unit is more subjected to the power grid peak regulation and frequency modulation tasks. The control characteristics of large hysteresis, large inertia, nonlinearity and the like of the main steam pressure of the thermal power unit are particularly obvious under the deep peak regulation working condition, so that the problems of slow main steam pressure control response, large dynamic deviation and the like brought by the control characteristics are used for severely restricting the peak regulation climbing rate of the thermal power unit, and the safe and stable operation of the thermal power unit is also not facilitated.
At present, a coordinated control system based on boiler following is generally adopted in a thermal power generating unit, namely, a boiler regulates main steam pressure and a steam turbine regulates load. The main control of the boiler adopts a PID regulator, and differential feedforward of the pressure deviation of the main steam, feedforward of the load instruction of the unit and the like are designed. For subcritical thermal power units, a boiler master control PID generates a fuel master control regulating instruction and sends the fuel master control regulating instruction to a fuel master control, and the fuel master control PID is responsible for generating the fuel instruction and is used as a coal feeder (direct-fired pulverizing system) or a pulverized coal feeder (medium-stored pulverizing system). The fuel main control PID is a quick follow-up regulator, the parameter setting is simple and convenient, and the regulation quality can generally meet the requirement of fuel quantity regulation precision; the regulated quantity of the main control PID of the boiler is the main steam pressure of the boiler, and the setting of the regulating parameters plays a decisive role in regulating quality of the main steam pressure. In-situ experiments show that under the deep peak regulation working condition, due to the change of the combustion characteristic of the boiler and the flow characteristic of the steam turbine, the main steam pressure control PID regulator adopting the conventional load section has poor parameter setting applicability and cannot meet the requirement of regulating quality, and an advanced optimization algorithm is required to be adopted to optimize the main steam pressure control system.
Disclosure of Invention
The invention aims to solve the technical problem of providing a method for optimizing the main steam pressure of a PID control thermal power unit under the deep peak-shaving working condition by an improved crowd optimization algorithm, wherein the main steam pressure of the thermal power unit is controlled by a method for optimizing PID control parameters based on an improved crowd search algorithm (Seeker Optimization Algorithm, SOA), so that the main steam pressure tracking set value has good performance and good robustness.
The technical scheme provided by the invention is as follows: a method for optimizing main steam pressure of a PID control thermal power depth peak shaver set by an improved crowd searching algorithm comprises the following steps:
(1) Establishing a main steam pressure identification transfer function model based on field test data;
(2) PID parameter optimization based on crowd searching algorithm;
(3) The original crowd searching algorithm is improved in a combined mode by adopting a reverse differential evolution mechanism and a self-adaptive t distribution strategy, parameter optimization is carried out on the main steam pressure control PID, the control parameters obtained through optimization are placed in the site DCS configuration, and the control effect is checked through a fixed value disturbance test.
Further, in the step (1), the main steam pressure identification modeling is based on the field data. And (3) performing experimental modeling on steady-state operation conditions of the thermal power generating unit with certain depth peak regulation, changing the fuel quantity fed into the furnace in a step-by-step mode, obtaining a main steam pressure response curve, and identifying by a least square method to obtain a main steam pressure transfer function model. The main steam pressure is a typical thermal process with self-balancing capability, and the model structure can be selected as formula (1) to obtain a transfer function model as shown in formula (2).
In the formula: k-static gain of the main steam pressure transfer function; n-transfer function order; τ -delay time; s-Laplace operator.
Further, in the step (2), the main steam pressure PID control parameters are optimized based on a crowd searching algorithm.
As can be seen from the formula (2), the main steam pressure model of the thermal power generating unit under the deep peak regulation working condition is a second-order inertia plus pure delay object, the on-site setting PID is mostly realized by adopting a trial-and-error method according to experience, the debugging process is long in time consumption, and the dynamic index of the adjusting process is not easy to reach the precision requirement.
At present, built-in PID modules of main stream DCS control systems at home and abroad all adopt parallel PID, and a control rule shown as a formula (3) is formed according to the deviation e (t) of an input value rin (t) and an output value yout. Equation (5) is a transfer function form of PID, wherein K p is a proportionality coefficient, T i is an integral time constant, T d is a differential time constant, and the three are tuning parameters to be optimized.
e(t)＝rin(t)-yout(t) (3)
E (t) -deviation of system output from desired output in the formula; u (t) -control output signal of PID.
And selecting a proper sampling period, discretizing the PID controller and the controlled object, and discretizing by adopting a built-in function c2d when using matlab to carry out algorithm programming simulation.
Parameter coding: taking three parameters of PID as one search individual, the dimension of the position vector of each individual is d=3, and S individuals in the population P are defined, so the population P can be represented by formula (6):
And (3) selecting a fitness function: the fitness function is a unique index for evaluating the individual quality in the search optimization process, and is also a tie for combining the SOA algorithm with the control system, and the algorithm is guided to continuously evolve towards a control target. The selection principle is as follows: in order to obtain excellent regulation dynamic characteristics, a time integral performance index of an absolute value of an error is adopted as a minimum objective function; to prevent excessive controller output from damaging the field device, a square term for control input is introduced; in order to avoid the overshoot phenomenon, moderate punishment control is adopted, and the overshoot is used as one of indexes. Thus, the objective function is shown in formula (7). The values of all weights are generally as follows: ω 1＝0.999,ω2＝0.001,ω3 =100.
Determination of search step size: the uncertain reasoning behavior of the SOA is to simulate intelligent searching behavior of a person by utilizing the approximation capability of a fuzzy system, so as to establish the relation between the objective function value and the step length. The fuzzy variable of the search step length is expressed by a Gaussian membership function.
In the above formula (8), u A is a Gaussian membership degree; x is an input variable; u and delta are membership function parameters. When the output variable exceeds [ u-3δ, u+3δ ], if the membership is smaller than 0.0111, it is negligible, so u min =0.0111 is set, and in order to obtain a faster convergence speed, u max =0.95 is set.
The linear membership function is adopted, the maximum membership degree u max =1 is arranged at the optimal position, the minimum membership degree u min =0.0111 is arranged at the worst position, and u is smaller than 1.0 at other positions, as shown in the formula (9).
In the above formula (9), u ij is the membership degree of the j-dimensional search space objective function value i; alpha ij is the search step size of the j-dimensional search space; δ ij is a gaussian membership function parameter whose value is determined by equations (10) and (11):
ω＝(itermax-iter)/itermax (11)
In the above formula, x min and x max are the positions of the minimum and maximum function values in the same population, respectively; omega is an inertial weight, linearly decreasing from 0.9 to 0.1 with increasing algebra; the item and item max are the current iteration number and the maximum iteration number, respectively.
Determination of search direction: by analyzing and modeling the human's Living, lithe and pre-acting behaviors, three directions of action are expressed as formulas (12) - (14):
Combining the above factors, determining the search direction by using three-direction random weighted geometric average, as in equation (15):
Wherein, AndRespectively isIs the best position in the model (a); for the i-th searching for the collective historical best location of the individual's neighborhood, Searching for the i-th best location that the individual has experienced so far; sign is a sign function; And Is a constant within [0,1 ]; ω is the inertial weight.
Updating the individual position: based on the above analysis, individual location updates can be made based on the search direction and step size, as shown in the following equation.
Δxij(t+1)＝αij(t)dij(t) (16)
xij(t+1)＝xij(t)+Δxij(t+1) (17)
Wherein Δx ij (t+1) is the position change amount of the ith searching individual at the next moment in the j-dimensional searching space, and x ij (t) is the position of the ith searching individual at the moment in the j-dimensional searching space.
Further, in the step (3), the improvement of the people group searching algorithm is as follows: the standard crowd searching algorithm is based on the mutual coordination between the experience gradient direction and uncertain reasoning behaviors, global searching is widely performed in the whole iterative process, and then rich priori knowledge is accumulated for mining global optimal solutions. However, the location update approach with the blur step as a guide tends to induce the algorithm to fall into a local optimum, resulting in searches that deviate from the optimum solution; the lack of information exchange among people is easy to reduce the diversity of people, and the development range of algorithms is further limited. Aiming at the problems, the original crowd searching algorithm is jointly improved by adopting a reverse differential evolution mechanism and a self-adaptive t distribution strategy, so that the capability of separating from local optimum is further enhanced.
For any individual x ij, its inverse solution is defined as
xi'j＝ubj+lbj-xij (18)
In the above equation, ub j and lb j are the upper limit and the lower limit of the j-th dimension search space, respectively. In order to further enhance crowd diversity, differential evolution (DIFFERENTIAL EVOLUTION ALGORITHM, DE) is adopted to update crowd positions, high-quality individuals are searched through mutation, crossover and selection strategies, and information exchange among the individuals is enhanced.
For each individual vector x i in the population, three different individual vectors are randomly selected for combination, resulting in variant individuals:
v i(t+1)＝xr1(t)+F(xr2(t)-xr3 (t)) (19) above, r 1,r2,r3 is the number of three different differential individuals within [1,0.5N ], where the scaling factor F is a random number within the range of [0,1 ].
Test subjects u ij (t+1) were constructed using a crossover operation, the construction method being:
Wherein rand generates random numbers between [0,1], and the cross probability factor CR ranges between [0,1 ]. f (x) is an fitness function, calculating a fitness value of a new individual after the crossover operation is generated, and if the new individual has better fitness, replacing the original individual:
And adaptively updating the global optimal position by using a variation factor taking the iteration number i as a system parameter, and selecting a position with higher quality based on a greedy selection algorithm and participating in the next iteration. The self-adaptive t distribution is a random parameter set integrating the advantages of Gaussian distribution and Cauchy distribution, and when the random parameter set is used as a variation factor to disturb a feasible solution, the algorithm can have certain local random search capacity so as to avoid sinking into local optimum. The adaptive mutation process of the optimal individual is shown in the following formula:
xbest(t+1)＝xbest(t)+xbest(t)*trnd(t) (22)
In the above formula, trnd is a self-adaptive t distribution parametric function, and can be called by a matlab function library; x best (t) is the current optimal solution, and x best (t+1) is the new solution after the adaptive t distribution variation.
In summary, the crowd search algorithm SOA performs parameter optimization on the main vapor pressure control PID according to the analysis flow. In order to verify the steady state and dynamic performance of the obtained regulating system, a set value step signal is adopted to excite the designed closed-loop control system.
The invention has the advantages that: compared with other intelligent optimizing algorithms, the improved SOA algorithm has clear meaning and simple parameter setting in the design process of adjusting the adaptability function, optimizing step length and direction, iteratively updating individual positions and the like in the PID parameter setting process, and the invention can directly model by adopting on-site actual data, so that the PID controller obtained by design has stronger pertinence to the production process and engineering practical value.
Drawings
FIG. 1 is a schematic diagram of the control principle of the present invention;
FIG. 2 is a graph of the primary steam pressure identification modeling in an embodiment of the present invention;
FIG. 3 is a flowchart of an algorithm of the present invention;
FIG. 4 is a graph showing PID parameter variation according to an embodiment of the invention;
FIG. 5 is a graph of the change in fitness function during SOA optimization in accordance with an embodiment of the present invention;
FIG. 6 is a graph of a step disturbance response for a main pressure set point in an embodiment of the present invention;
FIG. 7 is a graph of a step response composite comparison of a main pressure set point (SOA versus a modified SOA) in an example of the present invention;
FIG. 8 is a graph of the main steam pressure regulation process for deep regulation of a thermal power generating unit using the algorithm of the present invention.
Detailed Description
The application of the intelligent algorithm based on crowd searching optimization in the main steam pressure control system of the thermal power generating unit is described in detail by taking a 660MW deep peak-shaving thermal power generating unit as an implementation object and carrying out parameter optimization on the main steam pressure control system according to the technical method of the invention. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
The overall control principle of the present invention will be described with reference to fig. 1. The main steam pressure-fuel control object in the figure comprises a fuel instruction and a generalized control object for controlling the actual fuel quantity, and under the condition that the main fuel control parameter is certain, the modeling of the main steam pressure is simpler and more convenient. Taking the complexity and the calculated amount of the crowd searching algorithm into consideration, under the condition that the original control logic of the DCS and the structure of the PID controller basically remain unchanged, an offline parameter setting scheme is adopted. Under the off-line condition, the control system design is carried out on the controlled object of the main steam pressure transfer function obtained through identification, and the fitness function which is reasonably constructed is continuously and iteratively calculated through a crowd searching algorithm, so that a group of optimal PID control parameter values are obtained.
The main steam pressure control method under the deep peak-shaving working condition of the thermal power generating unit based on the crowd Searching Optimization Algorithm (SOA) specifically comprises the following steps:
(1) And (3) establishing a transfer function model of the main steam pressure object based on a system identification principle by adopting an on-site step disturbance test method. And cutting the main control of the unit boiler and the main control of the steam turbine into a manual state, changing a fuel main control instruction (5%) in a step mode, obtaining a main steam pressure response curve, and carrying out bad value elimination and zero mean value processing on the data. The known structural expression of the thermal process with typical self-balancing capability as shown in the formula (1) is selected, only parameters in the structural expression are needed to be identified, and the identification algorithm can be a least square and particle swarm optimization algorithm. The transfer function of the fuel quantity-main steam pressure process is in the form of a formula (2), and the identification result curve is shown in figure 2.
In the formula: k-static gain of the main steam pressure transfer function; n-transfer function order; τ -delay time; s-Laplace operator.
(2) And optimizing the PID control parameters of the main steam pressure based on a crowd searching algorithm.
As can be seen from the formula (2), the main steam pressure model of the thermal power generating unit under the deep peak regulation working condition is a second-order inertia plus pure delay object, the on-site setting PID is mostly realized by adopting a trial-and-error method according to experience, the debugging process is long in time consumption, and the dynamic index of the adjusting process is not easy to reach the precision requirement.
In the engineering case, a unit main control DCS adopts a parallel PID, and forms a control rule as shown in a formula (3) according to the deviation e (t) of an input value rin (t) and an output value yout. Equation (5) is a transfer function form of PID, wherein K p is a proportionality coefficient, T i is an integral time constant, T d is a differential time constant, and the three are tuning parameters to be optimized.
e(t)＝rin(t)-yout(t) (3)
And selecting a proper sampling period, discretizing the PID controller and the controlled object, and discretizing by adopting a built-in function c2d when using matlab to carry out algorithm programming simulation.
Parameter coding: taking three parameters of PID as one search individual, the dimension of the position vector of each individual is d=3, and S individuals in the population P are defined, so the population P can be represented by formula (6):
And (3) selecting a fitness function: the fitness function is a unique index for evaluating the individual quality in the search optimization process, and is also a tie for combining the SOA algorithm with the control system, and the algorithm is guided to continuously evolve towards a control target. The selection principle is as follows: in order to obtain excellent regulation dynamic characteristics, a time integral performance index of an absolute value of an error is adopted as a minimum objective function; to prevent excessive controller output from damaging the field device, a square term for control input is introduced; in order to avoid the overshoot phenomenon, moderate punishment control is adopted, and the overshoot is used as one of indexes. Thus, the objective function is shown in formula (7). The values of all weights are generally as follows: ω 1＝0.999,ω2＝0.001,ω3 =100.
Determination of search step size: the uncertain reasoning behavior of the SOA is to simulate intelligent searching behavior of a person by utilizing the approximation capability of a fuzzy system, so as to establish the relation between the objective function value and the step length. The fuzzy variable of the search step length is expressed by a Gaussian membership function.
In the above formula (8), u A is a Gaussian membership degree; x is an input variable; u and delta are membership function parameters. When the output variable exceeds [ u-3δ, u+3δ ], if the membership is smaller than 0.0111, it is negligible, so u min =0.0111 is set, and in order to obtain a faster convergence speed, u max =0.95 is set.
The linear membership function is adopted, the maximum membership degree u max =1 is arranged at the optimal position, the minimum membership degree u min =0.0111 is arranged at the worst position, and u is smaller than 1.0 at other positions, as shown in the formula (9).
In the above formula (9), α ij is the search step length of the j-dimensional search space; δ ij is a gaussian membership function parameter whose value is determined by equations (10) and (11):
ω＝(itermax-iter)/itermax (11)
In the above formula, x min and x max are the positions of the minimum and maximum function values in the same population, respectively; omega is an inertial weight, linearly decreasing from 0.9 to 0.1 with increasing algebra; the item and item max are the current iteration number and the maximum iteration number, respectively.
Determination of search direction: by analyzing and modeling the human's Living, lithe and pre-acting behaviors, three directions of action are expressed as formulas (12) - (14):
Combining the above factors, determining the search direction by using three-direction random weighted geometric average, as in equation (15):
Wherein, AndRespectively isIs the best position in the model (a); for the i-th searching for the collective historical best location of the individual's neighborhood, Searching for the i-th best location that the individual has experienced so far; sign is a sign function; And Is a constant within [0,1 ]; ω is the inertial weight.
Updating the individual position: based on the above analysis, individual location updates can be made based on the search direction and step size, as shown in the following equation.
Δxij(t+1)＝αij(t)dij(t) (16)
xij(t+1)＝xij(t)+Δxij(t+1) (17)
Wherein Δx ij (t+1) is the position change amount of the ith searching individual at the next moment in the j-dimensional searching space, and x ij (t) is the position of the ith searching individual at the moment in the j-dimensional searching space.
Improvement of crowd searching algorithm: the position updating mode with the fuzzy step length as the guide is easy to induce the algorithm to be in local optimum, thereby leading the search to deviate from the optimum solution; the lack of information exchange among people is easy to reduce the diversity of people, and the development range of algorithms is further limited. Aiming at the problems, the original crowd searching algorithm is jointly improved by adopting a reverse differential evolution mechanism and a self-adaptive t distribution strategy, so that the capability of separating from local optimum is further enhanced.
For any individual x ij, its inverse solution is defined as
xi'j＝ubj+lbj-xij (18)
In the above equation, ub j and lb j are the upper limit and the lower limit of the j-th dimension search space, respectively. In order to further enhance crowd diversity, differential evolution (DIFFERENTIAL EVOLUTION ALGORITHM, DE) is adopted to update crowd positions, high-quality individuals are searched through mutation, crossover and selection strategies, and information exchange among the individuals is enhanced.
For each individual vector x i in the population, three different individual vectors are randomly selected for combination, resulting in variant individuals:
vi(t+1)＝xr1(t)+F(xr2(t)-xr3(t)) (19)
In the above formula, r 1,r2,r3 is the number of three different differential individuals within [1,0.5N ], where the scaling factor F is a random number within the range of [0,1 ].
Test subjects u ij (t+1) were constructed using a crossover operation, the construction method being:
Wherein rand generates random numbers between [0,1], and the cross probability factor CR ranges between [0,1 ]. f (x) is an fitness function, calculating a fitness value of a new individual after the crossover operation is generated, and if the new individual has better fitness, replacing the original individual:
And adaptively updating the global optimal position by using a variation factor taking the iteration number i as a system parameter, and selecting a position with higher quality based on a greedy selection algorithm and participating in the next iteration. The self-adaptive t distribution is a random parameter set integrating the advantages of Gaussian distribution and Cauchy distribution, and when the random parameter set is used as a variation factor to disturb a feasible solution, the algorithm can have certain local random search capacity so as to avoid sinking into local optimum. The adaptive mutation process of the optimal individual is shown in the following formula:
xbest(t+1)＝xbest(t)+xbest(t)*trnd(t) (22)
In the above formula, x best (t) is the current optimal solution, and x best (t+1) is the new solution after the adaptive t distribution variation.
According to the steps, the parameter optimization of the improved crowd search algorithm SOA on the main steam pressure PID controller is realized, a parameter change curve in the iteration process is shown in fig. 4, and finally, parameters obtained by optimizing are Kp=8.32, ki=0.01 and Kd=52.1. The method combines the structure of the site DCS controller, converts the structure into an actual differential form, and places the parameter sequence into the site DCS logic configuration, so that relatively good control quality can be obtained. FIG. 7 is a graph showing a comprehensive comparison of step response of a main steam pressure set value in the example of the present invention, and it can be known that the step response output adjustment time of the control parameter obtained by the conventional SOA algorithm under the constraint of the control output condition is longer, and the static deviation is larger; the control parameters obtained by the improved SOA algorithm provided by the invention have the advantages of quick dynamic response, small static deviation, higher precision and quicker convergence in the set value step response process, and the superiority of the improved algorithm is verified. As shown in FIG. 8, in the coordinated variable load test process under the deep peak-shaving working condition of a certain supercritical unit, the actual main steam pressure tracking set value has good performance, and the control system index can meet the technical standard of the related industry.
The above description is merely illustrative of specific embodiments of the invention and is not intended to limit the invention, but any modifications, equivalents, improvements or alternatives falling within the spirit and scope of the invention are intended to be included within the scope of the invention.
Claims (4)
1. A method for optimizing main steam pressure of a PID control thermal power depth peak shaver set by an improved crowd searching algorithm is characterized by comprising the following steps:
(1) Establishing a main steam pressure identification transfer function model based on field test data;
(2) PID parameter optimization based on crowd searching algorithm;
(3) Carrying out joint improvement on an original crowd searching algorithm by adopting a reverse differential evolution mechanism and a self-adaptive t distribution strategy, carrying out parameter optimization on a main steam pressure control PID, putting the optimized control parameters into a site DCS configuration, and checking a control effect through a constant disturbance test;
the specific steps in the step (2) are as follows:
(a) For the parallel PID, a control rule as shown in a formula (3) is formed according to the deviation e (T) of an input value rin (T) and an output value yout (T), wherein a formula (5) is a transfer function form of the PID, K p is a proportionality coefficient, T i is an integral time constant, T d is a differential time constant, and the three are setting parameters to be optimized;
e(t)＝rin(t)-yout(t) (3)
e (t) -deviation of system output from desired output in the formula; u (t) -control output signal of PID;
Selecting a proper sampling period, discretizing a PID controller and a controlled object, and discretizing by adopting a built-in function c2d when using matlab to carry out algorithm programming simulation;
(b) Parameter coding: taking three parameters of PID as one search individual, the dimension of the position vector of each individual is d=3, defining S individuals in the population P, so the population P is represented by formula (6):
(c) And (3) selecting a fitness function: the fitness function is a unique index for evaluating the individual quality in the search optimization process, and is also a tie for combining the SOA algorithm with the control system, and the algorithm is guided to continuously evolve towards a control target; the selection principle is as follows: in order to obtain excellent regulation dynamic characteristics, a time integral performance index of an absolute value of an error is adopted as a minimum objective function; to prevent excessive controller output from damaging the field device, a square term for control input is introduced; in order to avoid the overshoot phenomenon, moderate punishment control is adopted, and the overshoot is used as one of indexes; thus, the objective function is shown as formula (7); wherein, the value of each weight is: ω 1＝0.999,ω2＝0.001,ω3 =100;
(d) Determination of search step size: the uncertain reasoning behavior of the SOA is to simulate intelligent searching behavior of a person by utilizing approximation capability of a fuzzy system, so as to establish connection between an objective function value and a step length, and a Gaussian membership function is adopted to represent a searching step length fuzzy variable;
In the above formula (8), u A is a Gaussian membership degree; x is an input variable; u and delta are membership function parameters, when the output variable exceeds [ u-3 delta, u+3 delta ], if the membership degree is smaller than 0.0111, u min =0.0111 is set, and in order to obtain a faster convergence rate, u max =0.95 is set;
A linear membership function is adopted, the maximum membership degree u max =1 is arranged at the optimal position, the minimum membership degree u min =0.0111 is arranged at the worst position, and u is smaller than 1.0 at other positions, as shown in a formula (9),
In the above formula (9), u ij is the membership degree of the j-dimensional search space objective function value i; alpha ij is the search step size of the j-dimensional search space; δ ij is a gaussian membership function parameter whose value is determined by equations (10) and (11):
ω＝(itermax-iter)/itermax (11)
In the above formula, x min and x max are the positions of the minimum and maximum function values in the same population, respectively; omega is an inertial weight, linearly decreasing from 0.9 to 0.1 with increasing algebra; the item and item max are the current iteration number and the maximum iteration number, respectively;
(e) Determination of search direction: by analyzing and modeling the human's Living, lithe and pre-acting behaviors, three directions of action are expressed as formulas (12) - (14):
Combining the above factors, determining the search direction by using three-direction random weighted geometric average, as in equation (15):
Wherein, AndRespectively isIs the best position in the model (a); For the i-th searching of the collective historical best position of the neighborhood of the individual Searching for the i-th best location that the individual has experienced so far; sign is a sign function; And Is a constant within [0,1 ]; omega is the inertial weight;
(f) Updating the individual position: according to the analysis, the individual position can be updated according to the searching direction and the step length, and the method is shown in the following formula;
Δxij(t+1)＝αij(t)dij(t) (16)
xij(t+1)＝xij(t)+Δxij(t+1) (17)
Wherein Deltax ij (t+1) is the position change quantity of the ith searching individual at the next moment of the j-dimensional searching space, and x ij (t) is the position of the ith searching individual at the moment of t in the j-dimensional searching space;
The specific steps of the step (3) are as follows:
for any individual x ij, its inverse solution is defined as
x′ij＝ubj+lbj-xij (18)
In the above formula, ub j and lb j are respectively the upper limit and the lower limit of the j-th dimension search space, in order to further enhance crowd diversity, it is proposed to update crowd positions by adopting differential evolution, and high-quality individuals are searched through mutation, intersection and selection strategies, so that information exchange among the individuals is enhanced;
For each individual vector x i in the population, three different individual vectors are randomly selected for combination, resulting in variant individuals:
vi(t+1)＝xr1(t)+F(xr2(t)-xr3(t)) (19)
In the above formula, r 1,r2,r3 is the number of three different differential individuals in [1,0.5N ], wherein the scaling factor F is a random number in the range of [0,1 ];
Test subjects u ij (t+1) were constructed using a crossover operation, the construction method being:
Wherein, rand generates random numbers between [0,1], the range of the crossover probability factor CR is between [0,1], f (x) is an fitness function, the fitness value of a new individual after crossover operation is calculated, if the new individual has better fitness, the original individual is replaced:
The global optimal position is adaptively updated by using a variation factor taking the iteration number i as a system parameter, and a position with higher quality is selected based on a greedy selection algorithm and participates in next iteration; the self-adaptive t distribution is a random parameter set integrating the advantages of Gaussian distribution and Cauchy distribution, and when the random parameter set is used as a variation factor to disturb a feasible solution, the algorithm can have certain local random searching capacity so as to avoid sinking into local optimum; the adaptive mutation process of the optimal individual is shown in the following formula:
xbest(t+1)＝xbest(t)+xbest(t)*trnd(t) (22)
In the above formula, trnd is a self-adaptive t distribution parametric function, and can be called by a matlab function library; x best (t) is the current optimal solution, and x best (t+1) is the new solution after the adaptive t distribution variation.
2. The method for optimizing the main steam pressure of the PID control thermal power depth peak shaver set by the improved crowd searching algorithm according to claim 1, wherein the method comprises the following steps:
In the step (1), test modeling is carried out on steady-state operation conditions of the deep peak-shaving thermal power generating unit, the fuel quantity fed into the furnace is changed in a step mode, a main steam pressure response curve and data are obtained, and a main steam pressure transfer function model is obtained through identification by a least square method, wherein the main steam pressure transfer function model is shown in the following formula:
In the formula: k-static gain of the main steam pressure transfer function; n-transfer function order; τ -delay time; s-Laplace operator.
3. The method for optimizing PID control over main steam pressure of a thermal power depth peak shaver set by an improved crowd search algorithm according to claim 1, wherein the crowd search algorithm SOA is a bionic control algorithm, and is proposed by analyzing random search behaviors of people; the optimal solution to the target is completed by simulating experience gradient and uncertainty reasoning in intelligent searching behaviors of the person; i.e. by describing natural language and modeling uncertainty reasoning in order to determine search steps, while determining search direction by autonomous learning capabilities and group behavior, including Litsea behavior, lithe behavior, self-organizing aggregate behavior, preventive behavior and uncertainty reasoning behavior.
4. The method for optimizing the main steam pressure of the PID control thermal power depth peak shaver set by the improved crowd searching algorithm according to claim 1, wherein the method comprises the following steps: the fitness function value in the step (2) is used for explaining whether an individual or a solution is good or bad in the optimization process of the crowd search algorithm SOA, and meanwhile, provides a reference for the position update of the individual, adopts an error absolute value time integral performance index as a minimum objective function of parameter selection, and adds a square term of control input quantity into the objective function in order to prevent the control quantity from being too large.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202211119376.5A CN115327890B (en) | 2022-09-14 | 2022-09-14 | Method for optimizing main steam pressure of PID control thermal power depth peak shaving unit by improved crowd searching algorithm |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202211119376.5A CN115327890B (en) | 2022-09-14 | 2022-09-14 | Method for optimizing main steam pressure of PID control thermal power depth peak shaving unit by improved crowd searching algorithm |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN115327890A CN115327890A (en) | 2022-11-11 |
| CN115327890B true CN115327890B (en) | 2024-07-16 |
Family
ID=83930285
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202211119376.5A Active CN115327890B (en) | 2022-09-14 | 2022-09-14 | Method for optimizing main steam pressure of PID control thermal power depth peak shaving unit by improved crowd searching algorithm |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN115327890B (en) |
Families Citing this family (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN118011783B (en) * | 2024-04-09 | 2024-06-04 | 天津仁爱学院 | Construction environment PID control method based on improved barrel jellyfish algorithm |
| CN118732630A (en) * | 2024-09-02 | 2024-10-01 | 朔韦茨环境科技(江苏)有限公司 | Method and system for optimizing nano film preparation process |
Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2010266967A (en) * | 2009-05-12 | 2010-11-25 | Fuji Electric Systems Co Ltd | Device and program for adjustment of pid |
| CN110308649A (en) * | 2019-07-11 | 2019-10-08 | 东南大学 | A kind of pid parameter optimization method based on PSO-SOA blending algorithm |
-
2022
- 2022-09-14 CN CN202211119376.5A patent/CN115327890B/en active Active
Patent Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2010266967A (en) * | 2009-05-12 | 2010-11-25 | Fuji Electric Systems Co Ltd | Device and program for adjustment of pid |
| CN110308649A (en) * | 2019-07-11 | 2019-10-08 | 东南大学 | A kind of pid parameter optimization method based on PSO-SOA blending algorithm |
Also Published As
| Publication number | Publication date |
|---|---|
| CN115327890A (en) | 2022-11-11 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN115327890B (en) | Method for optimizing main steam pressure of PID control thermal power depth peak shaving unit by improved crowd searching algorithm | |
| Khooban et al. | A new intelligent online fuzzy tuning approach for multi-area load frequency control: Self Adaptive Modified Bat Algorithm | |
| Cui et al. | Deep-neural-network-based economic model predictive control for ultrasupercritical power plant | |
| Karnavas et al. | AGC for autonomous power system using combined intelligent techniques | |
| Sati et al. | Two-Area Power System with Automatic Generation Control Utilizing PID Control, FOPID, Particle Swarm Optimization, and Genetic Algorithms | |
| Li et al. | Offset-free fuzzy model predictive control of a boiler–turbine system based on genetic algorithm | |
| CN110285403A (en) | Main Steam Temperature Control method based on controlled parameter prediction | |
| Banerjee et al. | Seeker optimization algorithm for load-tracking performance of an autonomous power system | |
| vE Conradie et al. | Adaptive control utilising neural swarming | |
| CN117784852B (en) | Multi-mode sensor temperature control method based on fish scale bionic optimization algorithm | |
| Wu et al. | GA-based nonlinear predictive switching control for a boiler-turbine system | |
| Li et al. | Load following control and global stability analysis for PWR core based on multi-model, LQG, IAGA and flexibility idea | |
| CN114909706B (en) | Two-level network balance regulation and control method based on reinforcement learning algorithm and differential pressure control | |
| CN118011805A (en) | Ultra-supercritical unit model predictive control method based on data driving and Tube optimization | |
| Singh et al. | Model predictive based load frequency control of interconnected power systems | |
| Hou et al. | Application of multi-agent EADRC in flexible operation of combined heat and power plant considering carbon emission and economy | |
| Zhu et al. | Stabilization and optimization of discrete-time Markovian jump linear systems via mode feedback control | |
| CN115289450A (en) | Full-load dynamically-adjusted boiler staged combustion real-time control method and system | |
| Wang et al. | Weight optimization for LQG controller based on the artificial bee colony algorithm | |
| Garduno-Ramirez et al. | Overall control of fossil-fuel power plants | |
| Gordillo et al. | Genetic design of a fuzzy logic controller for a solar power plant | |
| Negi et al. | Squirrel cage induction generator based micro grid voltage assessment with STATCOM using different metaheuristic approaches | |
| CN118494790B (en) | Ammonia working medium thruster thrust stability control method and system | |
| Icev et al. | Supervisory-plus-regulatory control design for efficient operation of industrial furnaces | |
| Mohammed et al. | Optimized Load Frequency Control for Single Area Power System Using Linear Quadratic Gaussian Technique and Coefficient Diagram Method |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |