CN109709797A - A kind of varying speed and constant pressure feed water control method and control system - Google Patents
A kind of varying speed and constant pressure feed water control method and control system Download PDFInfo
- Publication number
- CN109709797A CN109709797A CN201811615053.9A CN201811615053A CN109709797A CN 109709797 A CN109709797 A CN 109709797A CN 201811615053 A CN201811615053 A CN 201811615053A CN 109709797 A CN109709797 A CN 109709797A
- Authority
- CN
- China
- Prior art keywords
- particle
- constant pressure
- feed water
- control method
- pressure feed
- 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.)
- Pending
Links
Abstract
The present invention relates to a kind of varying speed and constant pressure feed water control method and control systems, the acquisition pattern for the closed loop PID control parameter that the control method uses are as follows: using each group of feasible closed loop PID control parameter as a three dimensional particles, search out optimal closed loop PID control parameter using quantum particle swarm optimization is improved；In the improvement quantum particle swarm optimization, introduces angle and make a variation and improve inertia weight update, obtain optimal closed loop PID control parameter.Compared with prior art, the present invention is adjusted using the controller parameter for improving quantum particle swarm optimization progress constant pressure water supply system, and global convergence speed is fast, and local convergence precision is high, can obtain preferably closed loop PID control parameter.
Description
Technical field
The present invention relates to the control of constant pressure water supply system optimization more particularly to a kind of varying speed and constant pressure feed water control methods
And control system.
Background technique
Constant pressure water supply control plays very important effect in national life, improves and supplies water, is energy-saving, improving speed regulation
Precision and power factor have important research significance.Meanwhile it is also to supply that reduction, mechanical wear and the noise of equipment loss, which reduce,
The indispensable consideration factor of water system, varying speed and constant pressure feed water mode have more advantage herein, and control purpose is to guarantee pipe
Net pressure of supply water is constant, balances water-supply quantity and water demand, while guaranteeing water supply quality and water, it is ensured that water system
It is safe and reliable to operation.
However, making rapid progress with science and technology, traditional control algorithm cannot provide user experience comfortable enough.
Traditional PID control parameter relies primarily on experience adjusting, not only time-consuming but also be unable to reach Optimal Control effect.In recent years, with
The development of intelligent algorithm and deeply, has scholar for constant pressure water supply system and proposes fuzzy and Control Strategy with Neural Network, such as become
Domain fuzzy control, ANN Control based on ant group optimization etc. compare traditional PID control certain improvement in effect,
But control strategy is complex, adaptability is not strong.
Currently, have the report that the colony intelligences optimization algorithms such as population (PSO) are applied to water system control both at home and abroad,
Such as document " the urban water supply configuration model optimization algorithm based on population " (Zhang Yongxiang, Wang Huifeng, Wang Hao, Tang Ying Beijing industries
College journal, 2016,42 (03): 467-472) by improved PSO algorithm be used for urban water supply configuration with dispatch use water, document
“Improved performance of PSO with self-adaptive parameters for computing the
optimal design of Water Supply Systems”(del Montalvo,Izquierdo,Rafael
Pe′rez-Manuel Herrera.Engineering Applications of Artificial
Intelligence, 2010,23 (05): 727-735) and " Particle swarm optimization applied to
the design of water supply systems”(Idel Montalvoa,Izquierdoa,Rafael
Perez′a,Michael M.Tungb.Computers and Mathematics with Applications,2008,56
(03): 769-776) improved PSO algorithm is used for the adaptive adjustment of parameter in Design of Waterworks.However, above-mentioned control
There is also strategy it is complex, easily fall into local extremum, low optimization accuracy is lower the deficiencies of.
Summary of the invention
A kind of frequency control constant pressure confession is provided the purpose of the invention is to overcome the problems of the above-mentioned prior art
Water controling method and control system.
The purpose of the present invention can be achieved through the following technical solutions:
A kind of varying speed and constant pressure feed water control method, the acquisition pattern for the closed loop PID control parameter that this method uses are as follows:
Using each group of feasible closed loop PID control parameter as a three dimensional particles, searched for using quantum particle swarm optimization is improved
Optimal closed loop PID control parameter out；
The improvement quantum particle swarm optimization specifically includes the following steps:
1) initial population is generated；
2) solution space converts；
3) fitness value that each particle is calculated with the fitness function of setting, calculate current particle individual optimal value and
Global optimum；
4) global and personal best particle is updated；
5) particle state is updated based on adaptive inertia weight；
6) rotation angle is introduced, is made a variation using Quantum rotating gate to the particle in population；
7) judge whether to meet stop condition, if so, thening follow the steps 8), if it is not, then return step 2)；
8) optimal particle is exported, optimal closed loop PID control parameter is obtained.
Further, in population, the position of the particle is indicated in the form of probability amplitude:
In formula, θijArgument, i, j=1,2 ..., n, α are tieed up for the jth of i-th of particlei、βiRespectively i-th particle it is remaining
String and sinusoidal position, indicate in quantum state | 0 > and | 1 > on probability amplitude.
Further, in the step 2), to each particle [α being distributed in [- 1,1]i,βi] carry out solution space transformation:
In formula, aj、bjIt is the lower and upper limit of the jth dimension Optimal Parameters of i-th of particle respectively,Respectively i-th
The cosine of the jth dimension Optimal Parameters of a particle and sine space position.
Further, in the step 3), integral square error type fitness function is multiplied using the time and calculates each particle
Fitness value.
Further, in the step 4), adaptive inertia weight is obtained by following formula:
In formula, w is adaptive inertia weight, fb、fwRespectively indicate best and worst fitness value, wmin、wmaxIt respectively indicates
Minimum and maximum weight, f (x) are the fitness value of current particle.
Further, the value range of the adaptive inertia weight is [0.1,0.3].
Further, in the step 5), rotation angle is taken as pi/2.
The present invention also provides a kind of Frequency Converted, which utilizes the control method
Realize varying speed and constant pressure feed water control.
Compared with prior art, the invention has the following advantages:
1, the method for the present invention searches out optimal closed loop PID control parameter, energy by improving quantum particle swarm optimization
Enough effectively improve the precision of varying speed and constant pressure feed water control.
2, the present invention, which improves, implements angle variation and inertia weight update in quantum particle swarm optimization, to enrich grain
Sub- diversity accelerates global convergence speed and improves local convergence precision, can obtain preferably closed loop PID control parameter.
3, the present invention is defined the rotation angle of variation, can guarantee the convergence for improving quantum particle swarm optimization
Performance efficiently avoids local convergence.
4, the present invention is defined the more new range of inertia weight, can guarantee to improve quantum particle swarm optimization
Constringency performance.
5, compared with the conventional method, the present invention has more significant effect of optimization in constant pressure water supply system adjusting, is protecting
Card gives better control characteristic under the premise of stablizing.
Detailed description of the invention
Fig. 1 is Frequency conversion speed adjusting structural schematic diagram；
Fig. 2 is the flow diagram that the present invention improves quantum particle swarm optimization；
Fig. 3 is constant pressure water supply control block diagram of the invention；
Fig. 4 is system step response and the noiseproof feature improved under quanta particle swarm optimization Different Variation angle, wherein
1—π/8；2—π/4；3—3π/8；4—π/2；5—5π/8；6—3π/4；7—7π/8；8—π；
Fig. 5 is the corresponding system step response of different weights more new formula and noiseproof feature, wherein 1-traditional weight
It updates w ∈ [0.1,0.3]；2-, which improve weight, updates w ∈ [0.1,0.3]；
Fig. 6 is system step response and the noiseproof feature improved under quanta particle swarm optimization difference inertia weight range,
Wherein, 1-w ∈ [0.0,0.2]；2—w∈[0.1,0.3]；3—w∈[0.2,0.4]；4—w∈[0.3,0.5]；5—w∈
[0.4,0.6]；6—w∈[0.5,0.7]；7—w∈[0.6,0.8]；8—w∈[0.7,0.9]；9—w∈[0.8,1.0]；
10-w=0.5；
Fig. 7 is to improve quanta particle swarm optimization and other algorithm comparison schematic diagrames, wherein (7a) is second-order inertia system；
(7b) is pure time delay system.
Specific embodiment
The present invention is described in detail with specific embodiment below in conjunction with the accompanying drawings.The present embodiment is with technical solution of the present invention
Premised on implemented, the detailed implementation method and specific operation process are given, but protection scope of the present invention is not limited to
Following embodiments.
1, varying speed and constant pressure feed water principle
Frequency conversion speed adjusting thereby guarantees that water supply and uses water flow usually using hydraulic pressure as standard control water flow
Amount balance.As shown in Figure 1, ductwork pressure is detected to obtain by sensor, pressure signal is converted to voltage signal through transmitter and transmits
To controller, controller is run according to current water pump operation situation and pressure signal by Frequency Converter Control water pump assembly, to reach
To pressure regulation purpose.More pump motor paired runnings, single motor starting default is usually arranged in Frequency conversion speed adjusting
Converting operation, insufficient pressure then increases frequency up to power frequency operation, while starting subsequent motor；Pressure is excessive, and it is straight to reduce frequency
To shutdown, while previous electric machine operation state is switched to frequency conversion by power frequency.
Frequency conversion speed adjusting need to be adjusted the output of corresponding frequency converter by controller according to user's urban water demand
Frequency (i.e. the pump motor speed of service) change makes water system pipe network water pressure reach user demand hydraulic pressure.Once pipe network water consumption
Change, constant pressure water supply system can change frequency converter output frequency automatically, to adjust the speed of service of pump motor, change system
Water supply.
If the design discharge of water system is Qs, rated lift Hs, when water system water supply reduces, due to ductwork pressure
Constant, lift is constant.According to general control valve control mode, since ductwork pressure reduces, lift will become larger.Water pump shaft work
Rate N can be described as:
In formula, ρ indicates the density of water, and Q is pipe network flow, and H is lift, and η indicates working efficiency.By formula as it can be seen that due to
Lift is constant, and varying speed and constant pressure feed water mode has better energy-saving effect and regulation performance.Meanwhile the constant-voltage characteristic of system
The loss to other equipment is reduced, is conducive to water system and runs reliably and with long-term.
2, quantum particle swarm optimization
Quantum particle swarm optimization (Quantum-Behaved Particle Swarm Optimization, QPSO)
By Sun " Quantum-behaved particle swarm optimization:analysis of the individual
particle behavior and parameter selection”(Jun Sun,Wei Fang,Xiaojun Wu,Vasile
Palade, Wenbo Xu.Evolutionary Computation, 2012,20 (03): 349-393) etc. propose, be that one kind is melted
The characteristics of algorithm of conjunction quantum calculation and PSO, the huge computing capability and PSO algorithm simple possible of incorporating quantum state operator, comes excellent
Change parameter, the particle in quantum state appears in any spatial point with certain probability, to find total optimization solution, reaches global
Restrain effect.
3, the method for the present invention
The present invention provides a kind of varying speed and constant pressure feed water control method, the closed loop PID control parameter that this method uses
Acquisition pattern are as follows: using each group of feasible closed loop PID control parameter as a particle, calculated using quantum telepotation is improved
Method (Improved Quantum-Behaved Particle Swarm Optimization, IQPSO) searches out optimal close
Ring pid control parameter reaches tracking error minimization, realizes parameter accurate convergence, improves quality of water supply.As shown in Fig. 2, improving
Quantum particle swarm optimization specifically includes the following steps:
1) initial population is generated, each position of particle is provided by probability amplitude form, is one group of cosine and sinusoidal position
It sets:
In formula, θijArgument is tieed up for the jth of i-th of particle, i, j=1,2 ..., n, n are particle number, αi、βiRespectively
The cosine and sinusoidal position of i particle, indicate in quantum state | 0 > and | 1 > on probability amplitude.
2) solution space converts, when initial population generates, particle [αi,βi] distribution in [- 1,1], in specific optimization problem
It need to be converted to obtain the feasible solution being distributed in feasible zone as corresponding linear according to specific range, that is, will be distributed over the particle of [- 1,1]
In the range of transforming to corresponding Optimal Parameters,
In formula, aj、bjIt is the lower and upper limit of jth dimension Optimal Parameters respectively, is in the present invention [Kp, Ki, Kd] value
Range,The respectively cosine of jth dimension Optimal Parameters and sine space position.
3) fitness value that each particle is calculated with the fitness function of setting, calculate current particle individual optimal value and
Global optimum.
Fitness function is the evaluation criterion of optimal control parameter, process, that is, optimal control parameter optimization to its optimizing
Process.The corresponding control parameter of minimum fitness function is optimal control parameter.The present invention selects the time to multiply square error product
Parting fitness function:
In formula, e (t) is absolute error.
In each iteration, the fitness value for calculating each particle compares and obtains current individual optimal value, through with it is global most
The figure of merit compares, and updates global optimum.
4) global and personal best particle is updated.
5) particle state is updated based on adaptive inertia weight.
Inertia weight represents succession of the current particle to speed, and value is big, and the ability of searching optimum of algorithm is good, conversely, office
Portion's search capability is good.Therefore, during searching for optimal solution, such as weight update is taken into account, has preferably convergence effect.
Previous weight updates, and often uses following more new formula:
In formula, wmin、wmaxRespectively indicate minimum and maximum weight, K, KmaxThe current iteration and maximum respectively set changes
Algebra.As the number of iterations increases, inertia weight tends to wmin.It, cannot since the formula is based only on the Serial regulation of the number of iterations
Convergence process is adapted to completely.
Adaptive inertia weight more new formula proposed by the present invention are as follows:
In formula, w is adaptive inertia weight, fb、fwRespectively indicate best and worst fitness value, wmin、wmaxIt respectively indicates
Minimum and maximum weight, f (x) are the fitness value of current particle.As result restrains, fitness function can be approached and is preferably adapted to
Degree even surmounts current optimal value, and inertia weight can then approach minimal weight, adapts to local search, this process and search optimal solution
Dynamic process be adapted, global convergence ability is more preferable.
The corresponding more new formula of each iteration of particle:
In formula, η1、η2Respectively itself and global factor, usually in [1,3] value, r1, r2It is the random number of [0,1], θij
It is the jth dimension argument of i-th of particle, Δ θl,ij、Δθg,ijIt is particle individual and global argument increment, is denoted as Δ θpij, adjust
Whole formula is as follows:
In formula, θp,ijIndicate θl,ij、θg,ij, θl,ij、θg,ijThe respectively jth of individual and global i-th of particle ties up argument.
6) rotation angle is introduced, is made a variation using Quantum rotating gate to the particle in population.
During QPSO algorithmic statement, although uniform particle be distributed in solution space, due to the aggregation of algorithm
Property, during finding optimal solution, loses population diversity and be not possible to avoid.For this purpose, the present invention is on original QPSO algorithm basis
On, particle variations are added to enrich population diversity, that is, introduce variation angle pass through Quantum rotating gate by particle probabilities width with
Certain proportion jumps out local convergence.It is shown below, by introducing a rotation angle θ, the general of particle is made using Quantum rotating gate
Rate width jumps out local convergence with certain proportion, wherein λ1、λ2Respectively indicate sin θ, cos θ:
7) judge whether to meet stop condition, if so, thening follow the steps 8), if it is not, then return step 2).
8) optimal particle is exported, optimal closed loop PID control parameter is obtained.
4, emulation experiment
The present embodiment combine programming and Matlab Simulink environment to the improvement factor of IQPSO algorithm implement verifying with
Obtain the optimal control parameter of constant pressure water supply system.Algorithm preset parameter is as follows: particle scale 50, the number of iterations 50 times, from
The body factor 2.6, global factor 1.4, Kp、Ki、KdValue range be [0,1].
Constant pressure water supply system has the characteristics that time-varying, the linearity are high, sluggish small, and system approximation model can be used with a second order
Property system replaces.The present embodiment takes the transmission function to beControl object, the control of whole system is asked
Topic regards a three-dimensional space (i.e. [K asp,Ki,Kd]) optimization problem calculated by selecting suitable fitness function using IQPSO
The optimal constant pressure water supply system controller parameter of optimizing, system structure are as shown in Figure 3 in space for method.Since noiseproof feature is
A key factor of constant pressure water supply system control superiority and inferiority is measured, so introducing a lasting 0.3s in simulation model main channel
The step disturbance that amplitude is 0.18.
4.1 Different Variation angles are to systematic influence
Variation angle is a key factor for influencing IQPSO algorithm optimization effect.Aberration rate is remained 0.01, from
0 to π with π/8 be equidistantly take respectively equal part point value be variation angle implement system control, fitness value is as shown in table 1, make a variation angle
It is best to restrain effect when pi/2.Its step response and noiseproof feature are as shown in Figure 4.When variation angle is pi/2, constant pressure water supply system
The rise time of system is shorter and non-overshoot, interference magnitude minimum have good dynamic characteristic and anti-interference；π/8,7 π/8 become
The corresponding control system in different angle has faster response speed and preferable stability, but anti-interference is poor and there are static differences；π/
4, the corresponding stability of control system in the variation of 3 π/4 angle and static characteristic are lacking；Remaining variation angle cannot achieve preferably
Control characteristic.
Table 1 improves the fitness value under quanta particle swarm optimization Different Variation angle
4.2 inertia weight update modes are to systematic influence
In order to compare the different influences to system control performance of inertia weight update mode, Application Range [0.1,0.3]
As weight more new range, result shown in table 2 and Fig. 5 is obtained for formula (5) and formula (6).The corresponding step of tradition more new formula
Response and anti-interference are not so good as to improve more new formula, and formula (6) realizes better control effect.
The corresponding fitness value of the different weight more new formulas of table 2
4.3 different inertia weight more new ranges are to systematic influence
By formula (6) it is found that the bound of weight more new formula, i.e. inertia weight more new range, to constant pressure water supply system
Control effect has a significant impact, so needing to carry out control optimization using parameter appropriate in simulations.The present embodiment is weighed with QPSO
Weight empirical value 0.5 is control, takes each Along ent to constitute to equidistant increment point for spacing equal part with 0.2 from 0 to 1
Value range is experimental group, and it is as shown in table 3 to obtain fitness value, [0.1,0.3] correspondence system minimum fitness.As shown in Figure 6,
Hold power focus on [0.1,0.3] update when, adjust stability and rapidity it is best, the effect of disturbance suppression is most ideal；Weight takes
When empirical value 0.5, the fluctuating range after system transient modelling process and disturbance is not up to most desired effect；Remaining more new range is corresponding
Constant pressure water supply system be lacking in either statically or dynamically characteristic.
Table 5 improves the fitness value under quanta particle swarm optimization difference inertia weight range
4.4 IQPSO are compared with other algorithm control effects
By afore-mentioned test, the IQPSO algorithm for optimizing Frequency conversion speed adjusting selects variation angle pi/2, weight
More new range [0.1,0.3], and compared with the same system controlled based on traditional PI D, PSO, QPSO algorithm.Document is " based on change
The research of domain Fuzzy-PID constant " (Zhang Yinhui Liaoning Technology University, 2012:9-25) refers to constant pressure water supply
The biography letter description with pure time delay of systemIt is imitated to verify IQPSO algorithm to the optimization that constant pressure water supply system controls
Fruit is emulated, as a result as shown in Figure 7 respectively for two class System describes.
In second-order inertia system, PID controller parameter adjusting is [0.7,0.1,0.01], PSO, QPSO, IQPSO adjusting
As a result it is respectively [1,0.078,0.00076], [0.534,0.056,0.00047] and [0.783,0.062,0.084], adapts to
Angle value is respectively 0.2833,0.2548,0.2332 and 0.1463.Pid algorithm adjusts control parameter by experience and repetition test,
The overshoot of system step response is larger, and disturbance amplitude is big, and anti-interference ability is poor；Its rank of the water system of PSO algorithm control
The overshoot of jump response is reduced, but anti-interference ability is not still strong；The water system step response of QPSO algorithm control has on a small quantity
Overshoot, disturbance waveform amplitude are big；Stability, rapidity, anti-interference most preferably IQPSO algorithm.
In pure time delay system, Kp、Ki、KdValue range be respectively [50,200], [0,10], [0,1], PID controller
Parameter tuning be [150,0.4,0.678], PSO, QPSO, IQPSO adjust result be respectively [175.65,0.561,0.762],
[178.72,0.5,0.9] and [192.3,1.1597,0.75], fitness value are respectively 0.1416,0.1025,0.0997 and
0.0840, four kinds of algorithm noiseproof features are very nearly the same；The rate of climb of IQPSO algorithm is most fast, the control of QPSO and PSO algorithm
Effect is close.Therefore, IQPSO algorithm has better adaptability, and advantage becomes apparent from.
The preferred embodiment of the present invention has been described in detail above.It should be appreciated that those skilled in the art without
It needs creative work according to the present invention can conceive and makes many modifications and variations.Therefore, all technology people in the art
Member passes through the available skill of logical analysis, reasoning, or a limited experiment on the basis of existing technology under this invention's idea
Art scheme, all should be within the scope of protection determined by the claims.
Claims (8)
1. a kind of varying speed and constant pressure feed water control method, which is characterized in that the closed loop PID control parameter that this method uses obtains
The mode of obtaining are as follows: using each group of feasible closed loop PID control parameter as a three dimensional particles, utilize and improve quantum telepotation
Algorithm search goes out optimal closed loop PID control parameter；
The improvement quantum particle swarm optimization specifically includes the following steps:
1) initial population is generated；
2) solution space converts；
3) fitness value that each particle is calculated with the fitness function of setting calculates the individual optimal value and the overall situation of current particle
Optimal value；
4) global and personal best particle is updated；
5) particle state is updated based on adaptive inertia weight；
6) rotation angle is introduced, is made a variation using Quantum rotating gate to the particle in population；
7) judge whether to meet stop condition, if so, thening follow the steps 8), if it is not, then return step 2)；
8) optimal particle is exported, optimal closed loop PID control parameter is obtained.
2. varying speed and constant pressure feed water control method according to claim 1, which is characterized in that in population, the particle
Position indicated in the form of probability amplitude:
In formula, θijArgument, i, j=1,2 ..., n, α are tieed up for the jth of i-th of particlei、βiThe cosine of respectively i-th particle and just
String position, indicate in quantum state | 0 > and | 1 > on probability amplitude.
3. varying speed and constant pressure feed water control method according to claim 1, which is characterized in that right in the step 2)
Each particle [the α being distributed in [- 1,1]i,βi] carry out solution space transformation:
In formula, aj、bjIt is the lower and upper limit of the jth dimension Optimal Parameters of i-th of particle respectively,Respectively i-th
The cosine of the jth dimension Optimal Parameters of son and sine space position.
4. varying speed and constant pressure feed water control method according to claim 1, which is characterized in that in the step 3), adopt
Multiply the fitness value that integral square error type fitness function calculates each particle with the time.
5. varying speed and constant pressure feed water control method according to claim 1, which is characterized in that in the step 4), from
Inertia weight is adapted to obtain by following formula:
In formula,wFor adaptive inertia weight, fb、fwRespectively indicate best and worst fitness value, wmin、wmaxRespectively indicate minimum
And weight limit, f (x) are the fitness value of current particle.
6. varying speed and constant pressure feed water control method according to claim 5, which is characterized in that the adaptive inertia power
The value range of weight is [0.1,0.3].
7. varying speed and constant pressure feed water control method according to claim 1, which is characterized in that in the step 5), rotation
Corner is taken as pi/2.
8. a kind of Frequency Converted, which is characterized in that real using control method as described in claim 1
Existing varying speed and constant pressure feed water control.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201811615053.9A CN109709797A (en) | 2018-12-27 | 2018-12-27 | A kind of varying speed and constant pressure feed water control method and control system |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201811615053.9A CN109709797A (en) | 2018-12-27 | 2018-12-27 | A kind of varying speed and constant pressure feed water control method and control system |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN109709797A true CN109709797A (en) | 2019-05-03 |
Family
ID=66257888
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201811615053.9A Pending CN109709797A (en) | 2018-12-27 | 2018-12-27 | A kind of varying speed and constant pressure feed water control method and control system |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN109709797A (en) |
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112926620A (en) * | 2021-01-21 | 2021-06-08 | 国网山东省电力公司检修公司 | Transformer bushing partial discharge defect type identification method and system |
| CN117452802A (en) * | 2023-11-08 | 2024-01-26 | 上海上源泵业制造有限公司 | Low-carbon water supply control method and system |
| CN117452802B (en) * | 2023-11-08 | 2024-05-14 | 上海上源泵业制造有限公司 | Low-carbon water supply control method |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20110269491A1 (en) * | 2010-04-30 | 2011-11-03 | Eberhart Russell C | Real-time optimization of allocation of resources |
| CN105426954A (en) * | 2015-08-20 | 2016-03-23 | 武汉科技大学 | Particle swarm optimization method based on multi-strategy synergistic function |
| CN105780714A (en) * | 2016-03-28 | 2016-07-20 | 武汉大学 | Combined water supply water right allocation scheduling method for reservoir group |
| CN106154836A (en) * | 2016-08-29 | 2016-11-23 | 西安西热控制技术有限公司 | A kind of online dynamic particles group's PID optimization method |
-
2018
- 2018-12-27 CN CN201811615053.9A patent/CN109709797A/en active Pending
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20110269491A1 (en) * | 2010-04-30 | 2011-11-03 | Eberhart Russell C | Real-time optimization of allocation of resources |
| CN105426954A (en) * | 2015-08-20 | 2016-03-23 | 武汉科技大学 | Particle swarm optimization method based on multi-strategy synergistic function |
| CN105780714A (en) * | 2016-03-28 | 2016-07-20 | 武汉大学 | Combined water supply water right allocation scheduling method for reservoir group |
| CN106154836A (en) * | 2016-08-29 | 2016-11-23 | 西安西热控制技术有限公司 | A kind of online dynamic particles group's PID optimization method |
Non-Patent Citations (9)
| Title |
|---|
| IDEL MONTALVO 等: "Improved performance of PSO with self-adaptive parameters for computing the optimal design of Water Supply Systems", 《ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE》 * |
| IDEL MONTALVO 等: "Particle Swarm Optimization applied to the design of water supply systems", 《COMPUTERS & MATHEMATICS WITH APPLICATIONS》 * |
| JUN SUN 等: "Quantum-Behaved Particle Swarm Optimization: Analysis of Individual Particle Behavior and Parameter Selection", 《EVOLUTIONARY COMPUTATION》 * |
| 于进 等: "基于PIC16F877A的变频恒压供水控制器设计", 《自动化仪表》 * |
| 张永祥 等: "基于粒子群的城市供水配置模型优化算法", 《北京工业大学学报》 * |
| 张银辉: "基于变论域模糊PID恒压供水控制系统的研究", 《中国优秀硕士学位论文全文数据库 信息科技辑》 * |
| 李盼池: "量子计算及其在智能优化与控制中的应用", 《中国博士学位论文全文数据库 信息科技辑》 * |
| 王超 等: "参数自适应粒子群算法的给水管网优化研究", 《智能系统学报》 * |
| 陈占伟 等: "一种自适应惯性权重的粒子群优化算法", 《微电子学与计算机》 * |
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112926620A (en) * | 2021-01-21 | 2021-06-08 | 国网山东省电力公司检修公司 | Transformer bushing partial discharge defect type identification method and system |
| CN117452802A (en) * | 2023-11-08 | 2024-01-26 | 上海上源泵业制造有限公司 | Low-carbon water supply control method and system |
| CN117452802B (en) * | 2023-11-08 | 2024-05-14 | 上海上源泵业制造有限公司 | Low-carbon water supply control method |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Xi et al. | Automatic generation control based on multiple neural networks with actor-critic strategy | |
| Xu et al. | Adaptive condition predictive-fuzzy PID optimal control of start-up process for pumped storage unit at low head area | |
| Xu et al. | An adaptively fast fuzzy fractional order PID control for pumped storage hydro unit using improved gravitational search algorithm | |
| CN108490790A (en) | A kind of overheating steam temperature active disturbance rejection cascade control method based on multiple-objection optimization | |
| Nayak et al. | Study of tuning of PID controller by using particle swarm optimization | |
| CN106570579B (en) | Water, fire and electricity economic dispatching method based on improved quantum particle swarm algorithm | |
| CN105888970B (en) | The adaptive inner mould vibration control method that intelligent blower blade is optimized based on grey information | |
| Li et al. | Chaotic particle swarm optimization algorithm based on adaptive inertia weight | |
| CN109709797A (en) | A kind of varying speed and constant pressure feed water control method and control system | |
| CN107701361A (en) | A kind of wave-activated power generation method and system | |
| CN110492483A (en) | A kind of primary frequency modulation feedback channel nonlinear element method for parameter configuration and system | |
| CN112994024B (en) | Load frequency control method and system with improved temperature control load participation | |
| CN110277785A (en) | Electrical couplings system loading margin calculation method and system based on continuous multipotency stream | |
| Liu et al. | Deep-Q-network-based intelligent reschedule for power system operational planning | |
| Wang et al. | Design of fractional order pi controller for permanent magnet synchronous motor | |
| Yang et al. | Application of fuzzy PID control in the heating system | |
| Sun et al. | Optimization of FOPID controller based on MPFGA | |
| Chen et al. | The application of intelligent integral realized by fuzzy logic for hydroturbine governing system | |
| Wu et al. | FREQUENCY H 2/H∞ OPTIMIZING CONTROL FOR ISOLATED MICROGRID BASED ON IPSO ALGORITHM. | |
| Li et al. | Parameter optimization for turbine DEH control system based on Improved Genetic Algorithm | |
| CN105262107A (en) | Wind power grid connected reactive optimization method | |
| Li et al. | Data-Driven Control for Proton Exchange Membrane Fuel Cells: Method and Application | |
| Jiang et al. | The excitation switching control method of tidal generator based on ts fuzzy weighting | |
| Zhang et al. | GCHP system optimal predictive control based on RBFNN and APSO algorithm | |
| Sultan | Optimal control of load frequency control power system based on particle swarm optimization technique |
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 | ||
| RJ01 | Rejection of invention patent application after publication |
Application publication date: 20190503 |
|
| RJ01 | Rejection of invention patent application after publication |