CN114254571A - Machine set control rule optimization decision method under extreme working conditions of pumped storage power station - Google Patents
Machine set control rule optimization decision method under extreme working conditions of pumped storage power station Download PDFInfo
- Publication number
- CN114254571A CN114254571A CN202111536242.9A CN202111536242A CN114254571A CN 114254571 A CN114254571 A CN 114254571A CN 202111536242 A CN202111536242 A CN 202111536242A CN 114254571 A CN114254571 A CN 114254571A
- Authority
- CN
- China
- Prior art keywords
- power station
- unit
- value
- storage power
- control rule
- 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.)
- Granted
Links
- 238000000034 method Methods 0.000 title claims abstract description 52
- 238000005457 optimization Methods 0.000 title claims abstract description 25
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 claims abstract description 59
- 230000008569 process Effects 0.000 claims abstract description 24
- 230000007704 transition Effects 0.000 claims abstract description 22
- 238000004422 calculation algorithm Methods 0.000 claims abstract description 20
- 230000002068 genetic effect Effects 0.000 claims abstract description 13
- 238000013178 mathematical model Methods 0.000 claims abstract description 7
- 238000012216 screening Methods 0.000 claims abstract description 4
- 239000002245 particle Substances 0.000 claims description 81
- 239000011159 matrix material Substances 0.000 claims description 23
- 230000000739 chaotic effect Effects 0.000 claims description 17
- 238000013507 mapping Methods 0.000 claims description 9
- 238000004364 calculation method Methods 0.000 claims description 8
- 238000005070 sampling Methods 0.000 claims description 7
- 230000006798 recombination Effects 0.000 claims description 6
- 238000005215 recombination Methods 0.000 claims description 6
- 230000001105 regulatory effect Effects 0.000 claims description 5
- 230000003044 adaptive effect Effects 0.000 claims description 4
- 230000000630 rising effect Effects 0.000 claims description 4
- 239000002131 composite material Substances 0.000 claims description 3
- 230000009191 jumping Effects 0.000 claims description 3
- 238000004540 process dynamic Methods 0.000 claims description 3
- 230000001934 delay Effects 0.000 claims description 2
- 230000010349 pulsation Effects 0.000 claims description 2
- 238000004088 simulation Methods 0.000 claims description 2
- 238000011144 upstream manufacturing Methods 0.000 claims description 2
- 239000012530 fluid Substances 0.000 abstract description 2
- 230000006870 function Effects 0.000 description 6
- 238000010586 diagram Methods 0.000 description 3
- 238000005259 measurement Methods 0.000 description 3
- 230000006872 improvement Effects 0.000 description 2
- 230000007246 mechanism Effects 0.000 description 2
- 230000001052 transient effect Effects 0.000 description 2
- 230000001133 acceleration Effects 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 238000006243 chemical reaction Methods 0.000 description 1
- 230000019771 cognition Effects 0.000 description 1
- 238000011217 control strategy Methods 0.000 description 1
- 238000012937 correction Methods 0.000 description 1
- 230000007547 defect Effects 0.000 description 1
- 238000004134 energy conservation Methods 0.000 description 1
- 238000004146 energy storage Methods 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
- 238000011156 evaluation Methods 0.000 description 1
- 230000005484 gravity Effects 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 230000001737 promoting effect Effects 0.000 description 1
- 238000011160 research Methods 0.000 description 1
- 239000004576 sand Substances 0.000 description 1
- 238000012795 verification Methods 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F30/00—Computer-aided design [CAD]
- G06F30/20—Design optimisation, verification or simulation
- G06F30/28—Design optimisation, verification or simulation using fluid dynamics, e.g. using Navier-Stokes equations or computational fluid dynamics [CFD]
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F2119/00—Details relating to the type or aim of the analysis or the optimisation
- G06F2119/14—Force analysis or force optimisation, e.g. static or dynamic forces
-
- 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
- Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
- Y02E10/00—Energy generation through renewable energy sources
- Y02E10/20—Hydro energy
Landscapes
- Engineering & Computer Science (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Theoretical Computer Science (AREA)
- Mathematical Physics (AREA)
- Fluid Mechanics (AREA)
- Mathematical Analysis (AREA)
- Mathematical Optimization (AREA)
- Computing Systems (AREA)
- Pure & Applied Mathematics (AREA)
- Computer Hardware Design (AREA)
- Evolutionary Computation (AREA)
- Geometry (AREA)
- General Engineering & Computer Science (AREA)
- Algebra (AREA)
- Control Of Water Turbines (AREA)
Abstract
The invention discloses a unit control rule optimization decision method under extreme working conditions of a pumped storage power station, and belongs to the technical field of fluid machinery and energy power. The method comprises the following steps: establishing a mathematical model of the extreme working condition transition process of the pumped storage power station, inputting a unit control rule, calculating the unit rotating speed, the volute water attack pressure extreme value and the draft tube water attack pressure extreme value, constructing a multi-target function of the dynamic quality of the unit transition process, and screening a unit control rule Pareto solution set by combining an improved non-dominated genetic algorithm. Further, a multi-criterion decision hierarchy framework of the unit control rule under the extreme working condition of the pumped storage power station is established, qualitative and quantitative indexes are integrated, and an optimal solution is selected in a unit control rule Pareto solution set based on an intuitive fuzzy analytic hierarchy process to serve as the optimal unit control rule under the extreme working condition of the pumped storage power station. The selected pump storage power station extreme working condition unit control rule can effectively improve the dynamic quality of the transition process of the pump storage power station under the extreme working condition, and improve the operation stability and safety of the power station.
Description
Technical Field
The invention belongs to the technical field of fluid machinery and energy power, relates to the technical field of pumped storage power station control, and particularly relates to a unit control rule optimization decision method under extreme working conditions of a pumped storage power station.
Background
In recent years, renewable intermittent energy sources such as wind and light are continuously and massively connected into a power grid, and the fluctuation of the renewable intermittent energy sources causes the stable operation of the power grid to be seriously threatened. As an important energy storage technology, the pumped storage power station is an effective and indispensable adjusting means of a power system, has the unique operation characteristics of peak load regulation and valley filling, and plays important functions of adjusting load, promoting energy conservation of the power system and maintaining safe and stable operation of a power grid. However, when the power grid or the unit fails, the water flow in the pipeline needs to be cut off in time. Under this extreme operating mode, the water conservancy transient can cause very big risk to the reliability of unit and pressure diversion system, is unfavorable for the safe high-efficient steady operation of unit. In addition, the pumped storage power station is developing towards a high water head, a large single machine capacity, a complex water passing system and an ultra-long water diversion pipeline, and the frequent working condition conversion also enables the unit to be more easily trapped in an extreme working condition, thereby greatly threatening the safe and stable operation of the pumped storage power station. In order to achieve the purpose of safe and stable operation of the unit, the control level of the pumped storage power station under the extreme working condition needs to be improved urgently.
Research proves that the closing rule of the pumped storage unit has great influence on the transition process, the reasonable closing rule can reduce the peak value of water hammer pressure and rotating speed in a certain range, and good dynamic quality of the transition process is obtained. In recent years, an optimization algorithm is applied to control rule optimization of a pumped storage power station under extreme working conditions, but the result obtained by the optimization algorithm is a group of Pareto solutions, and the quality relations of solutions in a concentration can not be compared. And the selection of the control law of the pumped storage power station is a multi-target multi-criterion decision problem essentially, and various dynamic qualities and other qualitative and quantitative factors in the transition process except the rotation speed extreme value and the pressure extreme value of the unit need to be comprehensively considered, so that the closing law of the unit is finally determined.
Disclosure of Invention
Aiming at the defects and improvement requirements of the prior art, the invention provides a method for optimizing and deciding a unit control rule under the extreme working condition of a pumped storage power station, and aims to select the optimal control rule of the pumped storage power station by multi-criterion decision taking the respective characteristics of various control strategies under the extreme working condition of the unit and qualitative and quantitative factors of dynamic quality of a transition process into consideration, and ensure safe and stable operation of a pumped storage system.
In order to achieve the purpose, the invention provides a unit control law optimization decision method under the extreme working condition of a pumped storage power station, which comprises the following steps:
(1) establishing a mathematical model of the extreme working condition transition process of a pumped storage power station, inputting a unit control rule, calculating a unit rotating speed, a volute water attack pressure extreme value and a draft tube water attack pressure extreme value, taking a unit transition process dynamic quality multi-objective function as an optimization target, screening a unit control rule Pareto solution set by combining an improved non-dominated genetic algorithm, fusing Latin hypercube sampling and piecewise linear chaotic mapping by the improved non-dominated genetic algorithm, and embedding an adaptive punishment strategy to solve the multiple decision space boundary limitation.
(2) Establishing a multi-criterion decision-making hierarchical framework of the unit control rules under the extreme working conditions of the pumped storage power station, integrating qualitative and quantitative indexes, and selecting an optimal solution in a unit control rule Pareto solution set as the optimal control rule of the unit under the extreme working conditions of the pumped storage power station.
Preferably, the mathematical model of the extreme working condition transition process of the pumped storage power station in the step (1) comprises a pressure water passing system, a water pump turbine, a speed regulator and a servo system thereof; the pressure water passing system comprises a pressure pipeline, a pressure adjusting chamber and a ball valve.
The pressure pipeline is described by adopting a continuous equation and a momentum equation, and the flow and pressure transient characteristics of the pressure pipeline are obtained by adopting a characteristic line method. The continuity equation and momentum equation have the following form:
the surge chamber has the form:
wherein HsAnd QsRespectively, the pressure and flow at the bottom of the pressure regulating chamber, AtAnd AcRespectively the cross-sectional area of the impedance hole of the pressure regulating chamber and the cross-sectional area of the pressure regulating chamber, HcAnd HRRespectively the head build-up and the hydraulic loss head, K, of the surge chamberRThe resistance hole hydraulic loss coefficient is related to the overflowing direction.
The mathematical expression of the ball valve is as follows:
a, Q represents the cross section area and the flow rate of the ball valve, K represents the flow coefficient of the ball valve, and g represents the gravity acceleration.
According to the pump turbine, the pump turbine total characteristic curve is used as nonlinear interpolation calculation of the pump turbine, an improved Suter variation method is introduced to process the unit total characteristic curve, and a binary ternary Lagrange interpolation method is adopted to improve calculation accuracy. The Suter variation and lagrange interpolation has the following form:
and (4) integrating the pressure water passing system, the water pump turbine, the speed regulator and a servo system thereof to obtain a transition process model of the pumped storage power station.
Preferably, the optimization objective in the step (1) is characterized by comprising a maximum relative value of the rotation speed rise of the unit and a composite extreme value of water hammer pressure;
the calculation form of the maximum rising relative value of the rotating speed of the unit is as follows:
wherein n is the number of units, xiIs the rotation speed of the ith unit, xr,iThe rated rotating speed of the ith unit;
the calculation form of the water hammer pressure composite extreme value is as follows:
wherein, Pvol,i、Pdra,iRespectively are the water hammer pressure values of the volute and the tail water pipe,
rated value for draft tube water hammer pressure, Iv、IdThe weight coefficients of the importance of the volute and the draft tube are respectively.
Preferably, the unit closing rule in the step (1) includes a linear closing rule of a guide vane section; closing rule of two sections of broken lines of the guide vanes; the guide vane ball valve is connected in tandem with two sections of closing rules; and the guide vane delays the closing rule of the two sections of broken lines of the linear ball valve.
One section straight line of stator closes law, characterized by: d1＝[ti](ii) a The rule is closed to two sections broken lines of stator, characterized by: d2＝[ti-1，ti-2,yi](ii) a Two sections of law of closing of stator ball valve antithetical couplet, characterized by: d3＝[ti-1，ti-2，yi，tbi-1，tbi-2，θi](ii) a The rule of closing the two sections of broken lines of the guide vane delay linear ball valve is as follows: d3＝[ti-s，ti-d，tbi-1，tbi-2，θi]. Wherein i is the number of the pumped storage power station units,ti-1,ti-2,yirespectively the first section of closing time, the second section of closing time and inflection point, t in the two-section broken line closing rule of the unitbi-1tbi-2θiThe first section of closing time, the second section of closing time and the inflection point in the two-section broken line closing rule of the unit ball valve are respectively. t is ti-s ti-dThe closing time and delay time of the unit for delaying a section of straight line closing are respectively set.
Preferably, the improved non-dominated genetic algorithm of step (1) comprises the steps of:
(1.1) setting the scale N of the particle population, the maximum iteration number T, the current iteration number T and the chaotic variation condition TchaoticDecision space dimension D, cross recombination probability PcProbability of polynomial variation Pm；
(1.2) executing a Latin hypercube sampling strategy to initialize a particle population, uniformly dividing a D-dimensional decision space into N non-overlapped equal-interval areas, randomly selecting a point from each equal-interval area of each dimension as a particle decision variable, and generating N initialization particles as parent particle swarm P;
(1.3) executing a self-adaptive penalty strategy according to the quantitative relation between feasible solutions and infeasible solutions in the current iteration cycle, and calculating a corrected optimization target value; the adaptive penalty policy has the form:
wherein f ism(Xi(t)) is the m-th optimized target value for the i-th particle, p (X)i(t)) is a penalty function added to the particle; the penalty function has two penalty factors M (X)i(t))、N(Xi(t)), calculated in the form of:
p(Xi(t))＝(1-rf)M(Xi(t))+rfN(Xi(t))
wherein,
as the sum of the i-th particle violations constraints,
respectively, the maximum and minimum optimal target values, r, of the feasible solutions in the particle populationfThe quantitative relation of the particle population is feasible;
(1.4) executing non-dominated sorting, generating a child particle swarm C from a parent particle swarm P through strategies such as tournament selection, cross recombination and polynomial variation, and calculating an optimized target value of particles in the child particle swarm C;
(1.5) judging whether the current iteration times T is greater than the chaotic variation condition TchaoticIf yes, executing the step (1.7), otherwise, turning to the step (1.7);
(1.6) performing piecewise linear chaotic mapping, wherein the piecewise linear chaotic mapping is calculated in the form of:
wherein p is an element (0, 0.5) as a control parameter, and x isiE (0,1) is a digital chaotic pseudorandom sequence;
(1.7) fusing the parent particle swarm P and the child particle swarm C to form a family particle swarm omega, and selecting N preferable particles from the family particle swarm omega to form an elite archive set E according to the non-dominated sorting and crowding degree; the preferred particles when selected follow the following principles: firstly, selecting particles with the highest non-dominated sorting level from family particle swarm omega, when the number of the particles in the same level is more than N, taking the crowding degree of the particles as a preferred basis, selecting the particles with smaller crowding degree to be put into an elite archive set E, and if the particle swarm with the highest non-dominated sorting level is less than N, selecting the particles from the particles with the next non-dominated sorting level until the elite archive set E contains N preferred particles;
(1.8) judging whether the current iteration time T is less than the maximum iteration time T, if so, jumping out of a loop, and taking the current elite archive set R as a Pareto solution set; otherwise, taking the current elite archive set R as the parent particle group P of the next cycle, and entering step (1.3).
Preferably, step (2) comprises the sub-steps of:
(2.1) set of blur (x | mu) in intuitionx,νx,πx) Comparing the decision-making hierarchical frames layer by layer from bottom to top to form an intuitive fuzzy hierarchical judgment matrix R for judging the base; wherein the value of the intuitive blur mux,νx,πxRespectively as membership degree, non-membership degree and hesitation degree;
(2.2) carrying out consistency check on the perceptual fuzzy hierarchical judgment matrix R, judging whether the judgment matrix meeting the consistency check is consistent with the judgment consistency, and turning to the step (2.4), or turning to the step (2.3);
(2.3) modifying the intuitionistic fuzzy level judgment matrix, and calculating the complete consistency matrix R of the intuitionistic fuzzy level judgment matrixpSelecting a control parameter sigma, and fusing R and RpTo obtain
Repeating the steps for multiple times until a judgment matrix meeting the consistency is obtained;
(2.4) fusing each layer of intuitive fuzzy level judgment matrix to obtain an intuitive fuzzy set of each scheme, and calculating the optimal weight rho of the scheme; the preferred weight is calculated in the form of: ρ (x) ═ 0.5(1+ πx)(1-μx) (ii) a The scheme with the smaller rho value has higher priority and higher override, and the scheme with the minimum optimal weight rho is selected as the optimal control rule of the unit under the extreme working condition of the pumped storage power station.
Preferably, the feasible solution and the infeasible solution in step (1.3) are characterized by: if the particle violates the multiple decision space boundary constraint, the particle is classified as an infeasible solution, otherwise the particle is classified as a feasible solution. The multiple decision space boundary limitations include rotational speed limitations, volute and ball valve pressure limitations, draft tube vacuum limitations, guide vane and ball valve operating speed limitations.
The ramp-up speed limit has the form: (x)i,max-xi,r)/xi,rLess than or equal to 45 percent, wherein xi,maxIs the maximum value of the rotation speed of the ith unit, xi,rThe rated value of the rotation speed of the ith unit;
the volute and ball valve water pressure limit has the following form:
wherein, Pvol,iThe water hammer pressure value of the volute of the ith unit,
is the initial value of the water hammer pressure h of the volute of the ith unitnIn order to be the working head of water,
is a volute water hammer pressure limit value;
the draft tube vacuum degree limit has the following form:
wherein, Pdra,iThe water hammer pressure value of the tail water pipe of the ith unit,
the initial value of water hammer pressure of the draft tube of the ith unitnIn order to be the working head of water,
is the water hammer pressure limit value of the draft tube;
the guide vane and ball valve operating speed limit is of the form:
wherein, DeltaY is the closing angle of the guide vane, TsimuIs a unit simulation step length. Y ismaxThe maximum opening degree of the guide vanes is,
for the fastest closing time of the guide vane, delta theta is the closing angle of the ball valve, thetamaxIs the maximum opening degree of the ball valve,
the fastest closing time for the ball valve.
In general, compared with the prior art, the above technical solution contemplated by the present invention can achieve the following beneficial effects:
(1) the method for optimizing the control law of the extreme working condition of the pumped storage power station embeds the improved multi-target non-dominated genetic algorithm, comprehensively considers a plurality of key targets of the transition process of the unit rotating speed and the pressure extreme value under the extreme working condition of the pumped storage power station, brings multiple criteria of the control law into decision evaluation basis, can solve the optimal control law of the unit under the extreme working condition by the method, can be applied to engineering practice, and obtains excellent dynamic quality of the transition process.
(2) The improved non-dominated genetic algorithm provided by the invention integrates the piecewise linear chaotic mapping of the Latin hypercube sampling machine and embeds the self-adaptive penalty strategy. In the algorithm initialization stage, a Latin hypercube sampling strategy is adopted, and initialization particle populations are uniformly and randomly generated in a decision space; a piecewise linear chaotic mapping mechanism is adopted in the middle and later stages of algorithm iteration to drive particles to jump out of a local optimal range and search other regions in a decision space; the proposed self-adaptive punishment strategy fully utilizes the proportional relation between feasible solutions and infeasible solutions under the current iteration and the information contained in the particles, and focuses on finding more feasible solutions in the initial stage of the algorithm and on finding more optimal solutions in the later stage of the algorithm.
(3) The intuitive fuzzy analytic hierarchy process is introduced into a multi-criterion decision framework, and the intuitive fuzzy value can fully reflect the important measurement of a decision maker on a target, comprises the affirmation, the negation and the hesitation of the decision maker and is more in line with the actual decision.
(4) The multi-criterion decision-making hierarchical framework of the extreme working condition of the pumped storage power station constructed by the invention fully considers the complexity and the safety of the dynamic quality and the control rule in the transition process of the pumped storage power station, and the obtained result is more comprehensive and more comprehensive.
Drawings
FIG. 1 is a schematic flow chart of a method for optimizing and deciding a unit control law in an extreme working condition of a pumped storage power station, which is disclosed by the embodiment of the invention;
FIG. 2 is a schematic flow chart of an improved non-dominated genetic algorithm disclosed in an embodiment of the invention;
FIG. 3 is a schematic diagram of a multi-criteria decision making process according to an embodiment of the present invention;
FIG. 4 is a multi-criterion decision-making hierarchical framework of the extreme condition control law of the pumped storage power station disclosed in the embodiment of the present invention;
FIG. 5 is a diagram illustrating intuitive fuzzy weights at a multi-criteria decision criteria level, in accordance with an embodiment of the present invention;
fig. 6 shows a transition process of a unit with a preferred control law under successive load shedding, where (a) is a rotation speed of a first unit, (b) is a rotation speed of a second unit, (c) is a volute pressure value of the first unit, (d) is a volute pressure value of the second unit, (e) is a draft tube vacuum degree of the first unit, and (f) is a draft tube vacuum degree of the second unit.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is described in further detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention. In addition, the technical features involved in the embodiments of the present invention described below may be combined with each other as long as they do not conflict with each other.
The invention provides a machine set control rule optimization decision method under the extreme working condition of a pumped storage power station, and aims to optimize the machine set closing rule under the extreme working condition of the pumped storage power station and reduce the safety risk of operation under the extreme working condition of the pumped storage power station.
Fig. 1 is a schematic flow chart of a method for optimizing and deciding a unit control law in a pumped storage power station under extreme conditions, according to an embodiment of the present invention, where the method shown in fig. 1 includes the following steps:
(1) establishing a mathematical model of the extreme working condition transition process of the pumped storage power station, inputting a unit control rule, calculating the unit rotating speed, the volute water attack pressure extreme value and the draft tube water machine pressure value, and screening a unit control rule Pareto solution set by combining an improved non-dominated genetic algorithm with a unit transition process dynamic quality multi-objective function as an optimization target;
(2) establishing a multi-criterion decision hierarchy framework of the unit control rule under the extreme working condition of the pumped storage power station, and selecting an optimal solution in a unit control rule Pareto solution set as the optimal control rule of the unit under the extreme working condition of the pumped storage power station.
Fig. 2 is a schematic flow chart of an improved non-dominated genetic algorithm disclosed in the embodiment of the invention, and the method shown in fig. 2 comprises the following steps:
(1.1) setting basic parameters of an optimization algorithm, specifically, setting the scale N of a particle population, the maximum iteration number T, the current iteration number T and a chaotic variation condition TchaoticDecision space dimension D, cross recombination probability PcProbability of polynomial variation Pm；
(1.2) initializing a particle parent population by using Latin hypercube sampling, specifically, uniformly dividing a D-dimensional decision space into N non-overlapped equal-interval areas, randomly selecting a point from each equal-interval area of each dimension as a particle decision variable, and generating N initialization particles as parent particle swarm P;
(1.3) executing a self-adaptive penalty function to correct the parent particle optimization target value, specifically, calculating a penalty factor according to the quantity relation of feasible solutions and infeasible solutions in the current iteration cycle and applying the penalty factor to the particle optimization target value;
(1.4) performing non-dominated sorting, tournament selection, cross recombination and polynomial variation to generate a progeny particle population, and calculating a progeny particle optimization target value;
(1.5) chaotic judgment, specifically, judging whether the current iteration number T is greater than the chaotic variation condition TchaoticIf yes, executing the step (1.6), otherwise, turning to the step (1.7);
(1.6) executing a piecewise linear chaotic mapping mechanism;
(1.7) fusing parent filial particle groups, performing rapid non-dominated genetic sorting based on the non-dominated sequence and the crowding degree distance, and selecting new parent particles;
(1.8) finishing the judgment, specifically, judging whether the current iteration time T is less than the maximum iteration time T, if so, jumping out of a loop, and taking the current solution set as a Pareto solution set; otherwise, taking the current solution set as the parent particle swarm P of the next loop, and entering step (1.3).
Fig. 3 shows a multi-criterion decision flow in a control law decision framework of a pumped storage power station under extreme conditions according to an embodiment of the present invention. And decomposing indexes, constructing a multi-level decision framework, and specifically dividing the control rule decision of the extreme working condition unit of the pumped storage power station into a target layer, a criterion layer and a scheme layer. The target layer is an optimal control rule of the extreme working conditions of the pumped storage power station; the criterion layer comprises quantitative indexes such as the maximum rotating speed rising value of the unit, the maximum water pressure value at the inlet of the volute, the vacuum pressure value at the outlet of the draft tube, the maximum water pressure value at the ball valve, the maximum oil speed of the controller and the ball valve, and qualitative indexes such as the rotating speed fluctuation degree of the unit, the pressure pulsation degree of the draft tube of the volute, the water level fluctuation degrees of the upstream and downstream pressure regulating chambers, the operation complexity of a control rule, the operation safety of the control rule and the like; the scheme layer is a Pareto solution set obtained by a multi-objective optimization algorithm, and a multi-level decision framework is shown in fig. 4.
In the flow shown in fig. 3, the following steps are included:
(2.1) comparing every two indexes of the same layer with an intuitionistic fuzzy set, specifically, carrying out comparison with the intuitionistic fuzzy set (x | mu)x,νx,πx) Comparing the decision-making hierarchical frames layer by layer from bottom to top to form an intuitive fuzzy hierarchical judgment matrix R for judging the base;
(2.2) consistency check and judgment, wherein the judgment matrix meeting the consistency check is considered to accord with the judgment consistency, the step (2.4) is carried out, and otherwise, the step (2.3) is carried out;
(2.3) performing consistency correction, specifically, modifying the intuitionistic fuzzy hierarchy judgment matrix, and calculating the intuitionistic modeComplete consistency matrix R of fuzzy level judgment matrixpSelecting a control parameter sigma, and fusing R and RpTo obtain
Repeating the steps for multiple times until a judgment matrix meeting the consistency is obtained;
and (2.4) fusing each layer of intuitionistic fuzzy hierarchy judgment matrix, calculating the optimal weight of the scheme, and selecting the scheme with the minimum optimal weight as the optimal control rule of the extreme working condition unit of the pumped storage power station.
As shown in fig. 5, the diagram of the intuitive fuzzy hierarchical weight of the target layer of the pumped storage power station under the extreme working conditions and with multiple criteria decision, according to the embodiment of the present invention, the vacuum degree and the operation safety of the draft tube are respectively the quantitative index and the qualitative index most concerned by the expert, and the importance obtained by the obtained intuitive fuzzy hierarchical ranking also meets the cognition of the expert.
The control law finally obtained by the optimization solution of the embodiment is applied to the working condition that two units enter load shedding successively in a one-pipe two-unit arrangement mode, the obtained result is shown in fig. 6, and the elite archive set comprises 40 individuals as shown in table 1. The ratio of the extreme values of the control law nodes of the successive load shedding working conditions to the actual measurement of the power station is shown in table 2. As can be seen from fig. 6 and table 2, the optimal control law of the unit obtained by the method meets the requirement of adjustment and calculation guarantee, a large margin is reserved, the rotating speed rise value of the unit and the water pressure value at the volute are greatly reduced, meanwhile, the vacuum degree at the draft tube is effectively improved, and the dynamic quality of the transition process is good. The verification shows that: the multi-objective optimization multi-criterion decision method for the control law under the extreme working condition of the pumped storage power station is used for optimizing and deciding the optimal control law under the extreme working condition of the pumped storage power station, and is correct and effective.
TABLE 1 external archive set and detailed parameters
TABLE 2 optimal control law, on-site actual measurement and standard node extreme value of regulation and protection calculation under successive load shedding working condition
It will be understood by those skilled in the art that the foregoing is only a preferred embodiment of the present invention, and is not intended to limit the invention, and that any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the scope of the present invention.
Claims (8)
1. A unit control law optimization decision method under the extreme working condition of a pumped storage power station is characterized by comprising the following steps:
(1) establishing a mathematical model of the extreme working condition transition process of a pumped storage power station, inputting a unit control rule, calculating a unit rotating speed, a volute water attack pressure extreme value and a draft tube water attack pressure extreme value, taking a unit transition process dynamic quality multi-objective function as an optimization target, screening a unit control rule Pareto solution set by combining an improved non-dominated genetic algorithm, fusing Latin hypercube sampling and piecewise linear chaotic mapping by the improved non-dominated genetic algorithm, and embedding an adaptive punishment strategy to solve the multiple decision space boundary limitation;
(2) establishing a multi-criterion decision-making hierarchical framework of the unit control rules under the extreme working conditions of the pumped storage power station, integrating qualitative and quantitative indexes, and selecting an optimal solution in a unit control rule Pareto solution set as the optimal control rule of the unit under the extreme working conditions of the pumped storage power station.
2. The method of claim 1, wherein the optimization objective comprises a combined extreme value of a maximum rise relative value of the unit rotation speed and the water hammer pressure;
the calculation form of the maximum rising relative value of the rotating speed of the unit is as follows:
wherein n is the number of units, xiIs the rotation speed of the ith unit, xr,iThe rated rotating speed of the ith unit;
the calculation form of the water hammer pressure composite extreme value is as follows:
wherein, Pvol,i、Pdra,iRespectively are the water hammer pressure values of the volute and the tail water pipe,
rated value for draft tube water hammer pressure, Iv、IdThe weight coefficients of the importance of the volute and the draft tube are respectively.
3. The method according to claim 2, wherein the improved non-dominated genetic algorithm comprises the steps of:
(1.1) setting the scale N of the particle population, the maximum iteration number T, the current iteration number T and the chaotic variation condition TchaoticDecision space dimension D, cross recombination probability PcProbability of polynomial variation Pm；
(1.2) executing a Latin hypercube sampling strategy to initialize a particle population, uniformly dividing a D-dimensional decision space into N non-overlapped equal-interval areas, randomly selecting a point from each equal-interval area of each dimension as a particle decision variable, and generating N initialization particles as parent particle swarm P;
(1.3) executing a self-adaptive penalty strategy according to the quantitative relation between feasible solutions and infeasible solutions in the current iteration cycle, and calculating a corrected optimization target value; the adaptive penalty policy has the form:
wherein f ism(Xi(t)) is the m-th optimized target value for the i-th particle, p (X)i(t)) is a penalty function added to the particle; the penalty function has two penalty factors M (X)i(t))、N(Xi(t)), calculated in the form of:
p(Xi(t))＝(1-rf)M(Xi(t))+rfN(Xi(t))
wherein,
as the sum of the i-th particle violations constraints,
respectively, the maximum and minimum optimal target values, r, of the feasible solutions in the particle populationfThe quantitative relation of the particle population is feasible;
(1.4) executing non-dominated sorting, generating a child particle swarm C from a parent particle swarm P through strategies such as tournament selection, cross recombination and polynomial variation, and calculating an optimized target value of particles in the child particle swarm C;
(1.5) judging whether the current iteration times T is greater than the chaotic variation condition TchaoticIf yes, executing the step (1.6), otherwise, turning to the step (1.7);
(1.6) performing piecewise linear chaotic mapping, wherein the piecewise linear chaotic mapping is calculated in the form of:
wherein p is an element (0, 0.5) as a control parameter, and x isi∈(0,1) Is a digital chaos pseudorandom sequence;
(1.7) fusing the parent particle swarm P and the child particle swarm C to form a family particle swarm omega, and selecting N preferable particles from the family particle swarm omega to form an elite archive set E according to the non-dominated sorting and crowding degree; the preferred particles when selected follow the following principles: firstly, selecting particles with the highest non-dominated sorting level from family particle swarm omega, when the number of the particles in the same level is more than N, taking the crowding degree of the particles as a preferred basis, selecting the particles with smaller crowding degree to be put into an elite archive set E, and if the particle swarm with the highest non-dominated sorting level is less than N, selecting the particles from the particles with the next non-dominated sorting level until the elite archive set E contains N preferred particles;
(1.8) judging whether the current iteration time T is less than the maximum iteration time T, if so, jumping out of a loop, and taking the current elite archive set R as a Pareto solution set; otherwise, taking the current elite archive set R as the parent particle group P of the next cycle, and entering step (1.3).
4. The method according to claim 1, wherein the pumped storage power station extreme condition unit control law multi-criterion decision hierarchy frame comprises a target layer, a criterion layer and a scheme layer; the target layer is an optimal control rule under the extreme working condition of the pumped storage power station; the standard layer comprises qualitative indexes and quantitative indexes, and the quantitative indexes comprise a set maximum rotating speed rising value, a maximum water pressure value at a volute inlet, a vacuum pressure value at a draft tube outlet, a maximum water pressure value at a ball valve, a controller and a ball valve maximum oil speed; the qualitative indexes comprise the fluctuation degree of the rotating speed of the unit, the pressure pulsation degree of a draft tube of the volute, the fluctuation degrees of the water levels of the upstream and downstream pressure regulating chambers, the operation complexity of a control rule and the operation safety of the control rule; the scheme layer is a Pareto solution set obtained by solving the multi-objective optimization scheme.
5. The method according to claim 4, wherein the decision-making unit control law is optimized based on an intuitive fuzzy analytic hierarchy process, and the method specifically comprises the following steps:
(2.1) set of blur (x | mu) in intuitionx,νx,πx) Comparing the decision-making hierarchical frames layer by layer from bottom to top to form an intuitive fuzzy hierarchical judgment matrix R for judging the base; wherein the value of the intuitive blur mux,νx,πxRespectively as membership degree, non-membership degree and hesitation degree;
(2.2) carrying out consistency check on the perceptual fuzzy hierarchical judgment matrix R, judging whether the judgment matrix meeting the consistency check is consistent with the judgment consistency, and turning to the step (2.4), or turning to the step (2.3);
(2.3) modifying the intuitionistic fuzzy level judgment matrix, and calculating the complete consistency matrix R of the intuitionistic fuzzy level judgment matrixpSelecting a control parameter sigma, and fusing R and RpTo obtain
Repeating the steps for multiple times until a judgment matrix meeting the consistency is obtained;
(2.4) fusing each layer of intuitive fuzzy level judgment matrix to obtain an intuitive fuzzy set of each scheme, and calculating the optimal weight rho of the scheme; the preferred weight is calculated in the form of: ρ (x) ═ 0.5(1+ πx)(1-μx) (ii) a The scheme with the smaller rho value has higher priority and higher override, and the scheme with the minimum optimal weight rho is selected as the optimal control rule of the unit under the extreme working condition of the pumped storage power station.
6. The method of claim 3, wherein the mathematical model of the pumped storage power station extreme condition transition process is provided with multiple decision space boundary limits; the multiple decision space boundary limitation comprises rotation speed limitation, volute and ball valve water pressure limitation, draft tube vacuum degree limitation, guide vane and ball valve operation speed limitation; if the particle violates any decision space boundary limit, the particle is divided into infeasible solutions, otherwise, the particle is divided into feasible solutions;
the ramp-up speed limit has the form: (x)i,max-xi,r)/xi,rLess than or equal to 45 percent, wherein xi,maxIs the ithMaximum value of rotational speed of the machine set, xi,rThe rated value of the rotation speed of the ith unit;
the volute and ball valve water pressure limit has the following form:
wherein, Pvol,iThe water hammer pressure value of the volute of the ith unit,
is the initial value of the water hammer pressure h of the volute of the ith unitnIn order to be the working head of water,
is a volute water hammer pressure limit value;
the draft tube vacuum degree limit has the following form:
wherein, Pdra,iThe water hammer pressure value of the tail water pipe of the ith unit,
the initial value of water hammer pressure of the draft tube of the ith unitnIn order to be the working head of water,
is the water hammer pressure limit value of the draft tube;
the guide vane and ball valve operating speed limit is of the form:
wherein, DeltaY is the closing angle of the guide vane, TsimuAs unit simulation step size, YmaxThe maximum opening degree of the guide vanes is,
the fastest closing time of the guide vane is, delta theta is the closing angle of the ball valve,θmaxis the maximum opening degree of the ball valve,
the fastest closing time for the ball valve.
7. The method of claim 1, wherein the mathematical model of the extreme condition transition process of the pumped storage power station comprises a pressure water passing system, a pump turbine full characteristic curve, a speed governor and a servo system thereof; the pressure water passing system comprises a pressure pipeline, a pressure adjusting chamber and a ball valve.
8. The method of claim 1, wherein the unit control law comprises a guide vane section straight closing law; closing rule of two sections of broken lines of the guide vanes; the guide vane ball valve is connected in tandem with two sections of closing rules; and the guide vane delays the closing rule of the two sections of broken lines of the linear ball valve.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202111536242.9A CN114254571B (en) | 2021-12-15 | 2021-12-15 | Method for optimizing and deciding unit control law under extreme working condition of pumped storage power station |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202111536242.9A CN114254571B (en) | 2021-12-15 | 2021-12-15 | Method for optimizing and deciding unit control law under extreme working condition of pumped storage power station |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN114254571A true CN114254571A (en) | 2022-03-29 |
| CN114254571B CN114254571B (en) | 2024-06-28 |
Family
ID=80795267
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202111536242.9A Active CN114254571B (en) | 2021-12-15 | 2021-12-15 | Method for optimizing and deciding unit control law under extreme working condition of pumped storage power station |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN114254571B (en) |
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115419540A (en) * | 2022-08-05 | 2022-12-02 | 华中科技大学 | Method and device for uniformly controlling multiple load shedding working conditions of pumped storage power station |
| CN115467858A (en) * | 2022-10-27 | 2022-12-13 | 四川三维鼓风机有限公司 | Motor stabilizing radiator for fan part |
| CN117193404A (en) * | 2023-09-28 | 2023-12-08 | 武汉船舶职业技术学院 | Multi-objective optimization control method for hydroelectric generating set model |
Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107480103A (en) * | 2017-07-10 | 2017-12-15 | 华中科技大学 | A kind of optimization method of pump-storage generator Pump Failure operating mode distributor closing law |
| WO2018072351A1 (en) * | 2016-10-20 | 2018-04-26 | 北京工业大学 | Method for optimizing support vector machine on basis of particle swarm optimization algorithm |
| CN108959739A (en) * | 2018-06-20 | 2018-12-07 | 国家电网有限公司 | A kind of analysis method and device of the pressure fluctuation of hydroenergy storage station transient process |
-
2021
- 2021-12-15 CN CN202111536242.9A patent/CN114254571B/en active Active
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2018072351A1 (en) * | 2016-10-20 | 2018-04-26 | 北京工业大学 | Method for optimizing support vector machine on basis of particle swarm optimization algorithm |
| CN107480103A (en) * | 2017-07-10 | 2017-12-15 | 华中科技大学 | A kind of optimization method of pump-storage generator Pump Failure operating mode distributor closing law |
| CN108959739A (en) * | 2018-06-20 | 2018-12-07 | 国家电网有限公司 | A kind of analysis method and device of the pressure fluctuation of hydroenergy storage station transient process |
Non-Patent Citations (1)
| Title |
|---|
| 于辉;周建中;王康生;赵志高;赖昕杰;许颜贺;: "基于精细化模型的水泵断电工况导叶关闭规律多目标优化", 水电能源科学, no. 07, 18 July 2018 (2018-07-18) * |
Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115419540A (en) * | 2022-08-05 | 2022-12-02 | 华中科技大学 | Method and device for uniformly controlling multiple load shedding working conditions of pumped storage power station |
| CN115467858A (en) * | 2022-10-27 | 2022-12-13 | 四川三维鼓风机有限公司 | Motor stabilizing radiator for fan part |
| CN115467858B (en) * | 2022-10-27 | 2024-08-27 | 四川三维风机制造有限责任公司 | Motor stable radiator for fan part |
| CN117193404A (en) * | 2023-09-28 | 2023-12-08 | 武汉船舶职业技术学院 | Multi-objective optimization control method for hydroelectric generating set model |
Also Published As
| Publication number | Publication date |
|---|---|
| CN114254571B (en) | 2024-06-28 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN114254571A (en) | Machine set control rule optimization decision method under extreme working conditions of pumped storage power station | |
| Pourrajabian et al. | Applying micro scales of horizontal axis wind turbines for operation in low wind speed regions | |
| CN109447405B (en) | One-bank multi-stage ladder level bank group short-term plan making method for bearing peak shaving task | |
| CN105207253B (en) | Consider wind-powered electricity generation and the probabilistic AGC stochastic and dynamics Optimization Scheduling of frequency | |
| CN105863948B (en) | A kind of band, which becomes, rises tailwater tunnel hydrogovernor variable parameter control method | |
| CN110598983A (en) | Cascade reservoir optimal scheduling method of self-adaptive improved particle swarm algorithm | |
| Lu et al. | Study on the optimal hydropower generation of Zhelin reservoir | |
| CN110059345A (en) | A kind of pump-storage generator successive load rejection ring-closing metathesis optimization method and system | |
| CN111476475B (en) | Short-term optimization scheduling method for cascade hydropower station under multi-constraint condition | |
| Hu et al. | Broadening the operating range of pump-turbine to deep-part load by runner optimization | |
| CN111861137A (en) | Parallel multi-target scheduling method for cascade reservoir groups | |
| CN114741961A (en) | Method and system for optimizing wing type fin arrangement structure of printed circuit board type heat exchanger | |
| CN114547987B (en) | Centrifugal pump turbine performance prediction method based on improved artificial neural network | |
| Liu et al. | Optimization and mechanism of the wicket gate closing law for high-head pumped storage power stations | |
| Li et al. | Optimal design of horizontal axis tidal current turbine blade | |
| Liu et al. | A many-objective optimization strategy for unified optimal operation of pumped storage hydropower station under multiple load rejection conditions | |
| CN118211502A (en) | Multi-objective optimization design method for aviation fuel centrifugal pump based on neural network | |
| CN107066686B (en) | Axial flow pump impeller hydraulic optimization design method based on genetic algorithm | |
| Bizzarrini et al. | Numerical optimization for high efficiency, low noise airfoils | |
| CN110610264A (en) | Single-target optimization algorithm for water supply network regulation and control under uncertain situation | |
| KR102439311B1 (en) | Coordinated optimization method for optimiztion of wind farm using sparsified wake digraph and apparatus performing the same | |
| Kocak et al. | Energy recovery using pumps as turbines in water supply systems: A case study | |
| KR102627896B1 (en) | A method for designing a portable hydroelectric generator having a high tsr and a design system for performing the same | |
| Sun et al. | Prediction and multi‐objective optimization of tidal current turbines considering cavitation based on GA‐ANN methods | |
| CN115455821A (en) | Centrifugal pump turbine performance prediction method based on improved PSO-GA algorithm |
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 |