CN108876002A - A kind of wind power generating set components standby redundancy inventory's optimization method - Google Patents
A kind of wind power generating set components standby redundancy inventory's optimization method Download PDFInfo
- Publication number
- CN108876002A CN108876002A CN201810417122.9A CN201810417122A CN108876002A CN 108876002 A CN108876002 A CN 108876002A CN 201810417122 A CN201810417122 A CN 201810417122A CN 108876002 A CN108876002 A CN 108876002A
- Authority
- CN
- China
- Prior art keywords
- components
- fault tree
- tree models
- event
- probability
- 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
- 238000005457 optimization Methods 0.000 title claims abstract description 37
- 238000000034 method Methods 0.000 title claims abstract description 27
- 238000004458 analytical method Methods 0.000 claims abstract description 7
- 238000010586 diagram Methods 0.000 claims description 13
- 230000007717 exclusion Effects 0.000 claims description 10
- 238000010521 absorption reaction Methods 0.000 claims description 4
- 238000005553 drilling Methods 0.000 claims description 4
- 238000010276 construction Methods 0.000 claims description 3
- 239000000284 extract Substances 0.000 claims description 3
- 230000005484 gravity Effects 0.000 abstract description 3
- 230000005611 electricity Effects 0.000 description 13
- 238000007726 management method Methods 0.000 description 6
- 230000007257 malfunction Effects 0.000 description 4
- 238000010248 power generation Methods 0.000 description 3
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 description 3
- 230000000694 effects Effects 0.000 description 2
- 239000004744 fabric Substances 0.000 description 2
- 238000012423 maintenance Methods 0.000 description 2
- 238000004519 manufacturing process Methods 0.000 description 2
- 241001282153 Scopelogadus mizolepis Species 0.000 description 1
- 238000005299 abrasion Methods 0.000 description 1
- 238000009825 accumulation Methods 0.000 description 1
- 125000003118 aryl group Chemical group 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 238000006243 chemical reaction Methods 0.000 description 1
- 239000003245 coal Substances 0.000 description 1
- 238000011217 control strategy Methods 0.000 description 1
- 230000001276 controlling effect Effects 0.000 description 1
- 238000005336 cracking Methods 0.000 description 1
- 238000000354 decomposition reaction Methods 0.000 description 1
- 230000001419 dependent effect Effects 0.000 description 1
- 238000004836 empirical method Methods 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
- 230000007613 environmental effect Effects 0.000 description 1
- 238000003912 environmental pollution Methods 0.000 description 1
- 238000009434 installation Methods 0.000 description 1
- 238000011835 investigation Methods 0.000 description 1
- 238000013138 pruning Methods 0.000 description 1
- 238000004451 qualitative analysis Methods 0.000 description 1
- 238000004445 quantitative analysis Methods 0.000 description 1
- 239000002994 raw material Substances 0.000 description 1
- 238000010845 search algorithm Methods 0.000 description 1
- 239000011265 semifinished product Substances 0.000 description 1
- 238000003860 storage Methods 0.000 description 1
- 201000009482 yaws Diseases 0.000 description 1
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/04—Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/08—Logistics, e.g. warehousing, loading or distribution; Inventory or stock management
- G06Q10/087—Inventory or stock management, e.g. order filling, procurement or balancing against orders
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/20—Administration of product repair or maintenance
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
- G06Q50/06—Energy or water supply
-
- 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
- Y04—INFORMATION OR COMMUNICATION TECHNOLOGIES HAVING AN IMPACT ON OTHER TECHNOLOGY AREAS
- Y04S—SYSTEMS INTEGRATING TECHNOLOGIES RELATED TO POWER NETWORK OPERATION, COMMUNICATION OR INFORMATION TECHNOLOGIES FOR IMPROVING THE ELECTRICAL POWER GENERATION, TRANSMISSION, DISTRIBUTION, MANAGEMENT OR USAGE, i.e. SMART GRIDS
- Y04S10/00—Systems supporting electrical power generation, transmission or distribution
- Y04S10/50—Systems or methods supporting the power network operation or management, involving a certain degree of interaction with the load-side end user applications
Landscapes
- Business, Economics & Management (AREA)
- Engineering & Computer Science (AREA)
- Economics (AREA)
- Human Resources & Organizations (AREA)
- Strategic Management (AREA)
- Physics & Mathematics (AREA)
- Tourism & Hospitality (AREA)
- Theoretical Computer Science (AREA)
- Marketing (AREA)
- General Physics & Mathematics (AREA)
- General Business, Economics & Management (AREA)
- Quality & Reliability (AREA)
- Operations Research (AREA)
- Entrepreneurship & Innovation (AREA)
- Development Economics (AREA)
- Health & Medical Sciences (AREA)
- Game Theory and Decision Science (AREA)
- Public Health (AREA)
- Water Supply & Treatment (AREA)
- General Health & Medical Sciences (AREA)
- Primary Health Care (AREA)
- Accounting & Taxation (AREA)
- Finance (AREA)
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
Abstract
The invention discloses a kind of wind power generating set components standby redundancy inventory's optimization methods.It includes the following steps：S1：Mode influences analysis is carried out to the failure behavior of wind power generating set, establishes corresponding fault tree models；S2：According to wind power generating set fault tree models, all minimal cut sets in fault tree models are found out；S3：According to the structure function of minimal cut set and fault tree models, the top event probability in fault tree models is solved；S4：The failure responsibility accounting for calculating different bottom events determines the corresponding components shortage of goods punishment loss of different bottom events；S5：Comprehensively consider components acquisition cost, warehouse cost and loss cost out of stock, establishes components Inventory Optimization Model；S6：Calculate the optimal solution of components Inventory Optimization Model.The present invention can rationally assess the responsibility specific gravity that different components undertake thrashing, establish Optimized model, solve the optimal Replenishment Policy of different spare parts.
Description
Technical field
The present invention relates to components storage controllings and logistics management technical field more particularly to a kind of wind power generating set zero
Component standby redundancy inventory's optimization method.
Background technique
Wind energy has as a kind of green non-pollution, reproducible new energy for solving environmental pollution and energy crisis
Important meaning.As wind generating technology constantly improves, global wind-powered electricity generation industry is quickly grown.According to Global Wind-energy council number
According to statistics, ending for the end of the year 2016, global wind-powered electricity generation accumulation installed capacity reaches 486749MW, and accumulative annual growth reaches 12.6%,
Wherein current year adding new capacity reaches 54600MW, and wherein, China is still the global country that takes the lead in race.China's installation total capacity
Up to 23378MW, the ratio that the market share accounts for the whole world is up to 43%, and the wind-driven generator kludge total capacity in China has been more than the U.S.
Leap to the first in the world.
THE WIND ENERGY RESOURCES IN CHINA is abundant, the wind energy content that can be developed and used about 1,000,000,000 thousand ten thousand, and wind energy is enriched band and is mainly distributed on
Northeast, North China, northwest and southeastern coast.In recent years, Wind Power In China industry emerges rapidly, and wind-powered electricity generation has become China after coal
The third-largest power supply after electricity, water power.
It is single with regard to unit cost of electricity-generating, wind-power electricity generation scientific and technological level and grid-connected reliability but if not considering Environmental costs
From the point of view of angularly, the competitiveness of wind-power electricity generation also can not show a candle to conventional energy resource power generation, and part wind-powered electricity generation enterprise also needs national government
Subsidy.
Unit cost of wind power generation is higher, is still that wind-power electricity generation is caused to compete a hypodynamic principal element.Study carefully its original
Cause is caused by wind field O&M cost accounting is larger, and O&M cost proportion is up to entire cost of electricity-generating in unit quantity of electricity
10%~30%, by taking the annual operation/maintenance data of certain wind field 2016 as an example, incomplete statistics, generated energy economic loss caused by failure
Total value is up to 3,200,000 yuans.But the existing wind field of China at present does not use scientific and effective components and spare parts management
Strategy, but still follow traditional empirical method, management mode is extensive, produce little effect, therefore formulate it is scientific and reasonable
Parts Inventory control strategy is a strong behave for reducing unit cost of wind power generation.
The main purpose of components part Parts Inventory management be guarantee big scale production equipment continual and steady operation, reduce by
Stall penalty is produced caused by the reasons such as damage of components or failure.Parts Inventory management is different from semi-finished product, raw material
Stock control, the quantity of spare part is not dependent on the demand of customer, but is determined by plant maintenance strategy, Replenishment Policy etc..
It, can be with using the desired value summation of acquisition cost, warehouse cost and shutdown loss cost as objective function by establishing Optimized model
Obtain the corresponding optimal Replenishment Policy of different components, the i.e. optimal value of Parts Inventory level.
But since structure is complicated for this large scale equipment of wind power generating set, there are complicated interdependent passes between components
The factor multiplicity of the system failure causes in system, and there is also corresponding substitutes for certain components, therefore rationally assessment is each
A components shortage of goods loss cost is to establish the committed step of Parts Inventory optimization problem.
Summary of the invention
The present invention to solve the above-mentioned problems, provides a kind of wind power generating set components standby redundancy inventory optimization side
Method is based on Fault Tree Analysis, system structure position and two angles of components historical failure rate locating for the components
Degree, rationally assesses the responsibility specific gravity that different components undertake thrashing, caused by determining that each components are out of stock
Then shutdown loss establishes Optimized model, solve the i.e. optimal Replenishment Policy of Optimal Inventory level of different spare parts.
To solve the above-mentioned problems, the present invention is achieved by the following scheme：
A kind of wind power generating set components standby redundancy inventory's optimization method of the invention, includes the following steps：
S1：Mode influences analysis is carried out to the failure behavior of wind power generating set, establishes corresponding fault tree models, failure
Top event in tree-model represents the system failure, and the bottom event in fault tree models represents some replaceable damage of components；
S2：According to wind power generating set fault tree models, all minimal cut sets in fault tree models are found out；
S3：According to the structure function of minimal cut set and fault tree models, the top event solved in fault tree models occurs generally
Rate；
S4：According to the probability of happening of bottom events different in fault tree models, the different bottoms that combination failure tree-model is shown
The logical construction relationship of event calculates the Bayes posterior probability of different bottom events, and then calculates the failure duty of different bottom events
Appoint accounting, caused macroeconomy is lost the corresponding components of determining different bottom events and lacked after finally top event being combined to break down
Goods punishment loss；
S5：Comprehensively consider components acquisition cost, warehouse cost and loss cost out of stock, establishes components inventory optimization mould
Type；
S6：Calculate the optimal solution of components Inventory Optimization Model.
Preferably, the step S1 includes the following steps：
Establish fault tree models, in fault tree models, capitalization represents all mutually independent failure causes, represents tool
The replaceable damage of components of body, the i.e. bottom event of fault tree models；The intermediate event of fault tree models is Mj, representing fault
Presentation or upper level event；The top event of fault tree models is T, represents the system failure；
In the fault tree models of wind power generating set, char [i] represents i-th of bottom event, eiIt represents and i-th of bottom thing
The value of the corresponding state variable of part, these state variables can determine by following formula,
Use vectorIndicate the combination of all state variables, then the structure function of fault tree models can be write
The state variable e of top event simultaneouslyTFor：
Preferably, the method for finding out all minimal cut sets in fault tree models in the step S2 includes following step
Suddenly：According to the structure function of boolean's idempotent law, boolean's absorption law and boolean's distributive law abbreviation fault tree models, according to the knot of abbreviation
Structure function acquires all minimal cut sets in fault tree models.
Preferably, the step S3 includes the following steps：
The relationship of top event probability and structure function is：
Due to that might not be mutex relation between minimal cut set, the joint of the minimal cut set of intersection non-empty be solved
When probability, the union using inclusion-exclusion principle by the union cylinder of all minimal cut sets for multiple mutual exclusion set is needed, then could
Probability totalization formula is enough used, i.e., does not hand over and equations, the formula of the inclusion-exclusion principle of set is as follows：
Wherein, AiFor the minimal cut set of fault tree models；(it can be seen that when fault tree popularization, minimal cut set number
Increase, the structure function based on minimal cut set form, which solves top event probability, can lead to the problem of multiple shot array namely probability tables
It is exponentially increased up to the adduction item number in formula.In order to avoid beyond limitation is calculated, the method for binary decision diagrams (bdds) is used for accurately
Solve top event probability)
According to ITE rule, by all minimal cut sets of above-mentioned fault tree models as subtree, and it is converted into binary decision
Figure；
In conjunction with Graph Theory and ITE rule, summation operation is carried out to fault tree subtree；It is (in ITE rule, it is specified that each
The left branch path of intermediate node represents node generation, and right branch path represents the node and do not occur, and leaf node is
" 1 " branches into effective branch)
The structure function of non cross link is obtained by Depth Priority Algorithm；
The mutual mutual exclusion of cut set composed by all adduction items in the structure function of non cross link, according to the structure letter of non cross link
Number, in conjunction with the probability sum formula of mutual exclusion set, obtains the probability expression of top event.
Preferably, the step S4 includes the following steps：
According to Bayes posterior probability formula：
And the top event probability obtained in conjunction with the obtained minimal cut set of step S2 and step S3, calculate different bottom events
Failure responsibility accounting：
Wherein, PjIndicate the failure responsibility accounting of j-th of bottom event, char [j] indicates j-th of bottom event, and Ω is all
The set of bottom event, Pr { char [j], T } indicate top event and the simultaneous probability of j-th of bottom event, Pr { char [i], T }
Indicate that top event and the simultaneous probability of i-th of bottom event, Pr { T } indicate the probability that top event occurs；
Caused macroeconomy loss C after finally top event being combined to break downtotal, determine that j-th of bottom event is corresponding
Components shortage of goods punishment loss pj,
Preferably, the step S5 includes the following steps：
Select (r, Q) Replenishment Policy as the basis of modeling, (r, Q) Replenishment Policy is：When inventory's water of a certain components
When putting down lower than threshold value r, it will disposably order the cargo that sum is Q, this stochastic variable of inventory level to parts manufacturer
Obeying value range is being uniformly distributed for { r+1, r+2 ..., r+Q }；
Assuming that the delivery time of ordering goods is a constant L not changed over time, components aggregate demand is D during replenishing,
Then the mean value of total demand is λ L, and wherein λ is the components demand factor in the unit time, during replenishing, the demand of components
Amount is substantially to be determined by component failure number, and the probability that components break down daily is identical, therefore can be by this
Process regards repetition L times Bernoulli trials as, and demand factor λ can also be replaced by failure rate, and due to failure rate and repetition
The magnitude very different of number is then approximately considered the components aggregate demand during replenishing and obeys the Poisson point that parameter is λ L
Cloth：
It is as follows finally to establish components Inventory Optimization Model：
Wherein, C (r, Q) is the desired value of the totle drilling cost in the unit time, and K is purchasing for the components cargo of Board Lot
Cost, h are warehouse cost of the unit item within the unit time, and p is the punishment out of stock of Board Lot components in the unit time
Loss；R and Q is the decision variable in optimization problem, and is nonnegative integer.
Preferably, the step S6 includes the following steps：
It extracts from components Inventory Optimization Model such as minor function：
- G (y) is a unimodal function about y, andThen the extreme point of G (y) is to be somebody's turn to do
Functional minimum value point, it is then available with lower inequality,
Wherein, y*For solve components Inventory Optimization Model globally optimal solution initial value,
By above-mentioned inequality, available y*Analytical expression：
Using obtained result as input, by following iterative algorithm, the optimal of blower components may finally be found out
(r, Q) Replenishment Policy, iterative algorithm are as follows：
Step1. according to y*Analytical expression calculate y*；
Step2. initial value assignment operation is carried out：qmin=y*, qmax=y*；
Step3. assignment operation is carried out：R=qmin- 1, Q=qmax-qmin+1；
Step4. if min { g (r), g (r+Q+1) } >=c (r, Q), then jump out program, otherwise jump to Step3；
Step5. if g (r)≤g (r+Q+1) }, carry out assignment operation：qmin=qmin- 1, otherwise carry out assignment operation：
qmax=qmax+1；
Step6. Step3 is jumped to.
The beneficial effects of the invention are as follows：Based on Fault Tree Analysis, the system structure position locating for the components and should
Two angles of components historical failure rate are rationally assessed the responsibility specific gravity that different components undertake thrashing, and are tied
Vehicles Collected from Market electricity price level, average generated output and the duration that averagely generates electricity are closed, with each components of determination stopping caused by out of stock
Machine loss, then establishes Optimized model, solves the i.e. optimal Replenishment Policy of Optimal Inventory level of different spare parts.
Detailed description of the invention
Fig. 1 is flow chart of the invention；
Fig. 2 is a kind of fault tree models exemplary diagram；
Fig. 3 is the conversion process figure of a kind of Minimizing Cut Sets of Fault Trees and binary decision diagrams (bdds)；
Fig. 4 is a kind of binary decision diagrams (bdds) final result figure；
Fig. 5 is a kind of wind power generating set fault tree models case diagram；
Fig. 6 is a kind of wind power generating set binary decision diagrams (bdds) result figure.
Specific embodiment
Below with reference to the embodiments and with reference to the accompanying drawing the technical solutions of the present invention will be further described.
Embodiment：A kind of wind power generating set components standby redundancy inventory's optimization method of the present embodiment, such as Fig. 1 institute
Show, includes the following steps：
S1：Mode influences analysis is carried out to the failure behavior of wind power generating set, establishes corresponding fault tree models, failure
Top event in tree-model represents the system failure, and the bottom event in fault tree models represents some replaceable damage of components；
S2：According to wind power generating set fault tree models, qualitative analysis is carried out, the structure function of abbreviation fault tree models is simultaneously
Find out all minimal cut sets in fault tree models；
S3：Quantitative analysis is carried out according to the structure function of minimal cut set and fault tree models, is solved in fault tree models
Top event probability；
S4：According to the probability of happening of bottom events different in fault tree models, the different bottoms that combination failure tree-model is shown
The logical construction relationship of event calculates the Bayes posterior probability of different bottom events, and then calculates the failure duty of different bottom events
Appoint accounting, caused macroeconomy is lost the corresponding components of determining different bottom events and lacked after finally top event being combined to break down
Goods punishment loss；
S5：Comprehensively consider components acquisition cost, warehouse cost and loss cost out of stock, establishes components inventory optimization mould
Type；
S6：Calculate the optimal solution of components Inventory Optimization Model.
Step S1 includes the following steps：
Establish fault tree models, in fault tree models, capitalization represents all mutually independent failure causes, represents tool
The replaceable damage of components of body, the i.e. bottom event of fault tree models；The intermediate event of fault tree models is Mj, representing fault
Presentation or upper level event；The top event of fault tree models is T, represents the system failure；
In the fault tree models of wind power generating set, char [i] represents i-th of bottom event, eiIt represents and i-th of bottom thing
The value of the corresponding state variable of part, these state variables can determine by following formula,
Use vectorIndicate the combination of all state variables, then the structure function of fault tree models can be write
The state variable e of top event simultaneouslyTFor：
Fault tree models can intuitively show all failure causes for leading to the system failure, and be able to reflect out difference
Failure cause asks complicated logical relation.
The method that all minimal cut sets in fault tree models are found out in step S2 includes the following steps：According to Boolean power etc.
The structure function of rule, boolean's absorption law and boolean's distributive law abbreviation fault tree models, acquires failure according to the structure function of abbreviation
All minimal cut sets in tree-model.
Boolean's idempotent law such as formula：AA=A, A+A=A,
Boolean's absorption law such as formula：A+AB=A,
Boolean's distributive law such as formula：A (B+C)=AB+AC.
Step S3 includes the following steps：
The relationship of top event probability and structure function is：
Due to that might not be mutex relation between minimal cut set, the joint of the minimal cut set of intersection non-empty be solved
When probability, the union using inclusion-exclusion principle by the union abbreviation of all minimal cut sets for multiple mutual exclusion set is needed, then could
Probability totalization formula is enough used, i.e., does not hand over and equations, the formula of the inclusion-exclusion principle of set is as follows：
Wherein, AiFor the minimal cut set of fault tree models；(it can be seen that when fault tree popularization, minimal cut set number
Increase, the structure function based on minimal cut set form, which solves top event probability, can lead to the problem of multiple shot array namely probability tables
It is exponentially increased up to the adduction item number in formula.In order to avoid beyond limitation is calculated, the method for binary decision diagrams (bdds) is used for accurately
Solve top event probability)
According to ITE rule, by all minimal cut sets of above-mentioned fault tree models as subtree, and it is converted into binary decision
Figure；
In conjunction with Graph Theory and ITE rule, summation operation is carried out to fault tree subtree；It is (in ITE rule, it is specified that each
The left branch path of intermediate node represents node generation, and right branch path represents the node and do not occur, and leaf node is
" 1 " branches into effective branch)
The structure function of non cross link is obtained by Depth Priority Algorithm；(it should be noted that searching for effective branch
During, the use of storehouse can be reduced using prune rule, improve search efficiency.Mainly by judging on active path
The content for being included whether there is contradiction, realize the beta pruning of algorithm)
The mutual mutual exclusion of cut set composed by all adduction items in the structure function of non cross link, according to the structure letter of non cross link
Number, in conjunction with the probability sum formula of mutual exclusion set, obtains the probability expression of top event.
The main purpose of the step is to be based on minimal cut set, will according to aromatic decomposition theorem and IF-THEN-ELSE rule
Fault tree models are converted into corresponding binary decision diagrams (bdds).Then binary decision diagrams (bdds) operation is carried out according to Graph Theory, and using deeply
First search algorithm is spent, obtains the non cross link form of structure function for calculating top event probability of malfunction.With binary decision diagrams (bdds)
Method can fast and effeciently calculate the probability of malfunction of top event, avoid mentioned above, be combined by inclusion-exclusion principle bring quick-fried
Fried problem.
Such as：A kind of fault tree models exemplary diagram shown in Fig. 2, there is 5 bottom events in fault tree models, respectively A, B,
C, D, E have 5 state variable e1, e2..., e5It is corresponding to it, then the structure function of fault tree models is：
It is according to 4 minimal cut sets that structure function obtains the fault tree：
{e1, e2, { e3, e4, { e1, e4, e5, { e2, e3, e5}。
According to ITE rule, by all minimal cut sets of above-mentioned fault tree models as subtree, and it is converted into binary decision
Figure, as shown in figure 3, wherein MCS refers to the minimal cut set acquired；
In conjunction with Graph Theory and ITE rule, to fault tree subtree carry out summation operation, obtain result as shown in figure 4,
It is by the structure function that Depth Priority Algorithm obtains non cross link：
The probability expression of top event is obtained in conjunction with the probability sum formula of mutual exclusion set according to the structure function of non cross link
Formula：
Step S4 includes the following steps：
According to Bayes posterior probability formula：
And the top event probability obtained in conjunction with the obtained minimal cut set of step S2 and step S3, calculate different bottom events
The failure responsibility accounting of (replaceable components)：
Wherein, PiIndicate the failure responsibility accounting of j-th of bottom event, char [j] indicates j-th of bottom event, and Ω is all
The set of bottom event, Pr { char [j], T } indicate top event and the simultaneous probability of j-th of bottom event, Pr { char [i], T }
Indicate that top event and the simultaneous probability of i-th of bottom event, Pr { T } indicate the probability that top event occurs；
Caused macroeconomy loss C after finally top event being combined to break downtotal, determine that j-th of bottom event is corresponding
Components shortage of goods punishment loss pi,
The main purpose of the step is to find out the accident responsibility ratio of different components, i.e., after assessment fan parking (or son
After the system failure), responsibility size shared by different components.It can be different in quantitative assessment complication system by this index
The punishment cost out of stock of components.
Step S5 includes the following steps：
Select (r, Q) Replenishment Policy as the basis of modeling, (r, Q) Replenishment Policy is：When inventory's water of a certain components
When putting down lower than threshold value r, it will disposably order the cargo that sum is Q, this stochastic variable of inventory level to parts manufacturer
Obeying value range is being uniformly distributed for { r+1, r+2 ..., r+Q }；
Assuming that the delivery time of ordering goods is a constant L not changed over time, components aggregate demand is D during replenishing,
Then the mean value of total demand is λ L, and wherein λ is the components demand factor in the unit time, during replenishing, the demand of components
Amount is substantially to be determined by component failure number, and the probability that components break down daily is identical, therefore can be by this
Process regards repetition L times Bernoulli trials as, and demand factor λ can also be replaced by failure rate, and due to failure rate and repetition
The magnitude very different of number is then approximately considered the components aggregate demand during replenishing and obeys the Poisson point that parameter is λ L
Cloth：
It is as follows finally to establish components Inventory Optimization Model：
Wherein, C (r, Q) is the desired value of the totle drilling cost in the unit time, and K is purchasing for the components cargo of Board Lot
Cost, h are warehouse cost of the unit item within the unit time, and p is the punishment out of stock of Board Lot components in the unit time
Loss；R and Q is the decision variable in optimization problem, and is nonnegative integer.
Since λ characterizes the components demand factor of unit time, the molecule of objective function first item is the unit time
Acquisition cost.Objective function Section 2 reflects warehouse cost desired value, when inventory level y is greater than aggregate demand i, needs
Parts thereof is stored.Objective function Section 3 reflects the desired value of punishment loss out of stock, since inventory level is not enough to
Meet the total demand amount during replenishing, results in the appearance of punishment loss out of stock.Eventually by the effect of denominator, objective function
It reflects in the unit time, the totle drilling cost desired value corresponding to Board Lot components.
In the practical logistics management of wind field, due to having corresponding discounting when pass on very substantial orders, wind field is generallyd use
The Replenishment Policy of batch type selects (r, Q) Replenishment Policy as the basis of modeling.During actual purchase, when cargo transport
Between be basically unchanged, therefore here consider order goods the delivery time be a constant L not changed over time.During replenishing, components
There is still a need for use and replace.
Step S6 includes the following steps：
It extracts from components Inventory Optimization Model such as minor function：
- G (y) is a unimodal function about y, andThen the extreme point of G (y) is to be somebody's turn to do
Functional minimum value point, it is then available with lower inequality,
Wherein, y*For solve components Inventory Optimization Model globally optimal solution initial value,
By above-mentioned inequality, available y*Analytical expression：
Using obtained result as input, by following iterative algorithm, the optimal of blower components may finally be found out
(r, Q) Replenishment Policy, iterative algorithm are as follows：
Step1. according to y*Analytical expression calculate y*；
Step2. initial value assignment operation is carried out：qmin=y*, qmax=y*；
Step3. assignment operation is carried out：R=qmin- 1, Q=qmax-qmin+1；
Step4. if min { g (r), g (r+Q+1) } >=c (r, Q), then jump out program, otherwise jump to Step3；
Step5. if g (r)≤g (r+Q+1) }, carry out assignment operation：qmin=qmin- 1, otherwise carry out assignment operation：
qmax=qmax+1；
Step6. Step3 is jumped to.
Citing：Below with certain rated power 2MW Large-scale Wind Turbines of certain wind-powered electricity generation limited liability company production
It the best inventory level of replaceable components and optimal replenishes for ground and pylon support subsystem, in the detailed analysis system
Strategy.
According to existing document and related O&M handbook, the fault tree mould of the wind power generating set ground built and tower system
Type, such as Fig. 5, the indicative explaination of corresponding event is as shown in table one, table two.
| Letter character | Replaceable components (bottom event) | Probability of malfunction statistics |
| A | Off-course brake (cracking) | 0.0137 |
| B | Yaw motor (failure) | 0.0110 |
| C | Yaw brake disc (abrasion) | 0.0055 |
| D | Mechanical anemometer (failure) | 0.0822 |
| E | Mechanical anemoscope (failure) | 0.0685 |
| F | It yaws electrical contact (failure) | 0.0027 |
One bottom event of fault tree probability of malfunction of table statistics
| Label code name | Intermediate event |
| G01 | Ground pylon failure |
| G02 | Yaw driving failure |
| G03 | Meteorological unit failure |
| G04 | Yaw motor current supply circuit failure |
Two fault tree intermediate event of table
According to principle described in step S2,5 minimal cut sets of the available fault tree are：{ A }, { B }, { C },
{ D, E }, { F },
Method is described according in step S3, minimal cut set is converted into corresponding binary decision diagrams (bdds), and eventually by graph theory
Method abbreviation obtain as a result, as shown in fig. 6,
By depth-first search, the non cross link form that original structure function can be obtained from binary tree is
Correspondingly, the expression formula of available top event probability is：
It, need to be with reference to the electric cost of the actual degree of wind-powered electricity generation and average odd-numbered day hair in order to calculate the punishment loss out of stock of different components
Electric duration determines economic loss caused by ground and tower system failure.Then according to the formula provided in step S4, knot
Minimal cut set, top event probability size are closed, the responsibility accounting of different components is calculated, finally decouples fan parking total losses
And it refine to the punishment loss out of stock of different components.
Investigation discovery, after generated energy loss in single day caused by support system failure is converted into economic benefit, about 18095
Member.The historical failure statistical probability of bottom event each in table is brought into formula, it is estimated that the probability of top event is
0.037957, the corresponding responsibility accounting size of each components in fault tree is calculated, as shown in Table 3.
| Letter character | Components | Failure rate | Responsibility accounting |
| A | Off-course brake | 0.0137 | 0.3104 |
| B | Yaw motor | 0.0110 | 0.2483 |
| C | Yaw brake disc | 0.0055 | 0.1241 |
| D | Mechanical anemometer | 0.0822 | 0.1276 |
| E | Mechanical anemoscope | 0.0685 | 0.1275 |
| F | Yaw loop contactor | 0.0027 | 0.0621 |
The responsibility accounting of three bottom event of fault tree of table
Finally, economic loss caused by top event failure and different bottom event responsibility accounting sizes are comprehensively considered, according to public affairs
FormulaCalculate Board Lot zero within the unit time
The punishment loss p out of stock of part, as shown in Table 4.In conjunction with different component failure rate sizes and different components shown in fig. 5
Logical relation can analyze out the validity for the responsibility accounting that table three provides：Although the failure rate of bottom event D and E are bigger,
Due to the special logical relation of the two ("AND"), the responsibility accounting being finally calculated not is maximum.
Parameter used in four Parts Inventory Optimized model of table
Parameter used in components Inventory Optimization Model simultaneously, including in the unit time components demand factor λ, order
Single delivery time L, the acquisition cost K of Board Lot components, the Board Lot components warehouse cost h in the unit time,
It enumerates in table four.
According to the optimization algorithm introduced in step S5, by program calculation, available difference bottom event is zero corresponding
Most preferably replenish threshold value r and the corresponding best quantity Q that replenishes of part, as shown in Table 5.
| Components | The threshold value that replenishes r | The quantity that replenishes Q |
| Off-course brake | 1 | 31 |
| Yaw motor | 0 | 12 |
| Yaw brake disc | 0 | 7 |
| Mechanical anemometer | 0 | 29 |
| Mechanical anemoscope | 0 | 27 |
| Yaw loop contactor | 0 | 3 |
The best Replenishment Policy of the different wind power generating set components spare parts of table five
It is comprehensive most preferably to replenish threshold value and single replenishes quantity, the optimal plan that replenishes of different blower components can be obtained
Slightly.For example, the single of off-course brake replenishes since punishment cost out of stock is higher, order delivery period is longer, carrying cost is lower
Quantity is more.Simultaneously in order to avoid the situation out of stock in order delivery period occurs, the threshold value that replenishes of the components is greater than zero, immediately
It carves and the components is kept to have spare part replaceable.
Claims (7)
1. a kind of wind power generating set components standby redundancy inventory's optimization method, which is characterized in that include the following steps：
S1：Mode influences analysis is carried out to the failure behavior of wind power generating set, establishes corresponding fault tree models, fault tree mould
Top event in type represents the system failure, and the bottom event in fault tree models represents some replaceable damage of components；
S2：According to wind power generating set fault tree models, all minimal cut sets in fault tree models are found out；
S3：According to the structure function of minimal cut set and fault tree models, the top event probability in fault tree models is solved；
S4：According to the probability of happening of bottom events different in fault tree models, the different bottom events that combination failure tree-model is shown
Logical construction relationship, calculate the Bayes posterior probability of different bottom events, and then the failure responsibility for calculating different bottom events accounts for
Than caused macroeconomy is lost the corresponding components shortage of goods of determining different bottom events and punished after finally top event being combined to break down
Penalize loss；
S5：Comprehensively consider components acquisition cost, warehouse cost and loss cost out of stock, establishes components Inventory Optimization Model；
S6：Calculate the optimal solution of components Inventory Optimization Model.
2. a kind of wind power generating set components standby redundancy inventory's optimization method according to claim 1, feature exist
In the step S1 includes the following steps：
Establish fault tree models, in fault tree models, capitalization represents all mutually independent failure causes, represents specific
Replaceable damage of components, the i.e. bottom event of fault tree models；The intermediate event of fault tree models is Mj, representing fault presentation
Or upper level event；The top event of fault tree models is T, represents the system failure；
In the fault tree models of wind power generating set, char [i] represents i-th of bottom event, eiIt represents and i-th of bottom event pair
The value of the state variable answered, these state variables can determine by following formula,
Use vectorIndicate the combination of all state variables, then the structure function of fault tree models can be writeSimultaneously
The state variable e of top eventTFor：
3. a kind of wind power generating set components standby redundancy inventory's optimization method according to claim 1, feature exist
In the method for finding out all minimal cut sets in fault tree models in the step S2 includes the following steps：According to Boolean power etc.
The structure function of rule, boolean's absorption law and boolean's distributive law abbreviation fault tree models, acquires failure according to the structure function of abbreviation
All minimal cut sets in tree-model.
4. a kind of wind power generating set components standby redundancy inventory's optimization method according to claim 2, feature exist
In the step S3 includes the following steps：
The relationship of top event probability and structure function is：
Due to that might not be mutex relation between minimal cut set, the joint probability of the minimal cut set of intersection non-empty be solved
When, the union using inclusion-exclusion principle by the union abbreviation of all minimal cut sets for multiple mutual exclusion set is needed, then can be made
With probability totalization formula, i.e., does not hand over and equations, the formula of the inclusion-exclusion principle of set are as follows：
Wherein, AiFor the minimal cut set of fault tree models；
According to ITE rule, by all minimal cut sets of above-mentioned fault tree models as subtree, and it is converted into binary decision diagrams (bdds)；
In conjunction with Graph Theory and ITE rule, summation operation is carried out to fault tree subtree；
The structure function of non cross link is obtained by Depth Priority Algorithm；
The mutual mutual exclusion of cut set composed by all adduction items in the structure function of non cross link, according to the structure function of non cross link, knot
The probability sum formula for closing mutual exclusion set, obtains the probability expression of top event.
5. a kind of wind power generating set components standby redundancy inventory's optimization method according to claim 1 or 2 or 3 or 4,
It is characterized in that, the step S4 includes the following steps：According to Bayes posterior probability formula：
And the top event probability obtained in conjunction with the obtained minimal cut set of step S2 and step S3, calculate the event of different bottom events
Hinder responsibility accounting：
Wherein, PjIndicate the failure responsibility accounting of j-th of bottom event, char [j] indicates j-th of bottom event, and Ω is all bottom events
Set, Pr { char [j], T } indicates that top event and the simultaneous probability of j-th of bottom event, Pr { char [i], T } indicate top
Event and the simultaneous probability of i-th of bottom event, Pr { T } indicate the probability that top event occurs；
Caused macroeconomy loss C after finally top event being combined to break downtotal, determine that j-th of bottom event is zero corresponding
Part shortage of goods punishment loss pj,
6. a kind of wind power generating set components standby redundancy inventory's optimization method according to claim 5, feature exist
In the step S5 includes the following steps：
Select (r, Q) Replenishment Policy as the basis of modeling, (r, Q) Replenishment Policy is：When the inventory level of a certain components is low
When threshold value r, it will disposably order the cargo that sum is Q to parts manufacturer, this stochastic variable of inventory level is obeyed
Value range is being uniformly distributed for { r+1, r+2 ..., r+Q }；
Assuming that the delivery time of ordering goods is a constant L not changed over time, components aggregate demand is D during replenishing, then needs
The mean value for seeking total amount is λ L, and wherein λ is the components demand factor in the unit time, during replenishing, the demand sheet of components
It is to be determined by component failure number, and the probability that components break down daily is identical, therefore can be by this process in matter
Regard repetition L times Bernoulli trials as, demand factor λ can also be replaced by failure rate, the components aggregate demand during replenishing
Amount obeys the Poisson distribution that parameter is λ L：
It is as follows finally to establish components Inventory Optimization Model：
Wherein, C (r, Q) is the desired value of the totle drilling cost in the unit time, and K is purchasing into for the components cargo of Board Lot
This, h is warehouse cost of the unit item within the unit time, and p is the punishment damage out of stock of Board Lot components in the unit time
It loses；R and Q is the decision variable in optimization problem, and is nonnegative integer.
7. a kind of wind power generating set components standby redundancy inventory's optimization method according to claim 6, feature exist
In the step S6 includes the following steps：
It extracts from components Inventory Optimization Model such as minor function：
- G (y) is a unimodal function about y, andThen the extreme point of G (y) is the function
Minimum point, it is then available with lower inequality,
Wherein, y*For solve components Inventory Optimization Model globally optimal solution initial value,
By above-mentioned inequality, available y*Analytical expression：
Using obtained result as input, by following iterative algorithm, optimal (r, the Q) of blower components may finally be found out
Replenishment Policy, iterative algorithm are as follows：
Step1. according to y*Analytical expression calculate y*；
Step2. initial value assignment operation is carried out：qmin=y*, qmax=y*；
Step3. assignment operation is carried out：R=qmin- 1, Q=qmax-qmin+1；
Step4. if min { g (r), g (r+Q+1) } >=c (r, Q), then jump out program, otherwise jump to Step3；
Step5. if g (r)≤g (r+Q+1) }, carry out assignment operation：qmin=qmin- 1, otherwise carry out assignment operation：qmax
=qmax+1；
Step6. Step3 is jumped to.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201810417122.9A CN108876002B (en) | 2018-05-03 | 2018-05-03 | Method for optimizing inventory of spare parts of wind generating set |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201810417122.9A CN108876002B (en) | 2018-05-03 | 2018-05-03 | Method for optimizing inventory of spare parts of wind generating set |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN108876002A true CN108876002A (en) | 2018-11-23 |
| CN108876002B CN108876002B (en) | 2021-08-17 |
Family
ID=64327567
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201810417122.9A Active CN108876002B (en) | 2018-05-03 | 2018-05-03 | Method for optimizing inventory of spare parts of wind generating set |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN108876002B (en) |
Cited By (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110287523A (en) * | 2019-05-16 | 2019-09-27 | 中国人民解放军海军工程大学 | The spare part scheme optimization method and device of multiple batches of component under modularization storage mode |
| CN112561411A (en) * | 2019-09-10 | 2021-03-26 | 上海杰之能软件科技有限公司 | Computing method of spare part safety inventory number, storage device and terminal |
| CN112633541A (en) * | 2019-09-24 | 2021-04-09 | 北京沃东天骏信息技术有限公司 | Inventory network optimization method and device based on single commodity flow |
| CN112668728A (en) * | 2019-10-15 | 2021-04-16 | 深圳怡化电脑股份有限公司 | Equipment module configuration method and device, computer equipment and storage medium |
| CN112766846A (en) * | 2021-01-11 | 2021-05-07 | 北京航空航天大学 | Spare part transfer network modeling and solving method |
| CN113822611A (en) * | 2020-07-16 | 2021-12-21 | 北京京东乾石科技有限公司 | Spare part management method and device, computer storage medium and electronic equipment |
| CN113933542A (en) * | 2021-10-14 | 2022-01-14 | 远景智能国际私人投资有限公司 | Anemometer fault detection method, device, equipment and storage medium |
| CN114080577A (en) * | 2019-07-12 | 2022-02-22 | 西门子工业软件有限责任公司 | Ring closure and normalized representation in fault trees |
| CN115511136A (en) * | 2022-11-01 | 2022-12-23 | 北京磁浮有限公司 | Equipment fault auxiliary diagnosis method and system based on hierarchical analysis and fault tree |
| CN115965140A (en) * | 2022-12-27 | 2023-04-14 | 北京航天智造科技发展有限公司 | Inventory optimal planning method, system, equipment and storage medium |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106050580A (en) * | 2016-08-17 | 2016-10-26 | 国电联合动力技术有限公司 | Method and system for diagnosing transmission chain fault of wind generating set |
| CN106401597A (en) * | 2016-10-27 | 2017-02-15 | 华中科技大学 | Failure prediction and diagnosis control method applicable to shield tunneling machine |
| CN106980913A (en) * | 2017-04-21 | 2017-07-25 | 浙江大学 | A kind of wind power generating set standby redundancy needing forecasting method based on failure tree analysis (FTA) |
| CN107609325A (en) * | 2017-10-18 | 2018-01-19 | 中国航空无线电电子研究所 | The method that fault tree based on SAT solves minimal cut set |
-
2018
- 2018-05-03 CN CN201810417122.9A patent/CN108876002B/en active Active
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106050580A (en) * | 2016-08-17 | 2016-10-26 | 国电联合动力技术有限公司 | Method and system for diagnosing transmission chain fault of wind generating set |
| CN106401597A (en) * | 2016-10-27 | 2017-02-15 | 华中科技大学 | Failure prediction and diagnosis control method applicable to shield tunneling machine |
| CN106980913A (en) * | 2017-04-21 | 2017-07-25 | 浙江大学 | A kind of wind power generating set standby redundancy needing forecasting method based on failure tree analysis (FTA) |
| CN107609325A (en) * | 2017-10-18 | 2018-01-19 | 中国航空无线电电子研究所 | The method that fault tree based on SAT solves minimal cut set |
Non-Patent Citations (1)
| Title |
|---|
| 陈棋等: ""某风电场风力发电机组故障诊断"", 《新能源及工艺》 * |
Cited By (12)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110287523A (en) * | 2019-05-16 | 2019-09-27 | 中国人民解放军海军工程大学 | The spare part scheme optimization method and device of multiple batches of component under modularization storage mode |
| CN114080577A (en) * | 2019-07-12 | 2022-02-22 | 西门子工业软件有限责任公司 | Ring closure and normalized representation in fault trees |
| CN112561411A (en) * | 2019-09-10 | 2021-03-26 | 上海杰之能软件科技有限公司 | Computing method of spare part safety inventory number, storage device and terminal |
| CN112561411B (en) * | 2019-09-10 | 2023-11-21 | 上海杰之能软件科技有限公司 | Method for calculating spare part safety stock number, storage equipment and terminal |
| CN112633541A (en) * | 2019-09-24 | 2021-04-09 | 北京沃东天骏信息技术有限公司 | Inventory network optimization method and device based on single commodity flow |
| CN112668728A (en) * | 2019-10-15 | 2021-04-16 | 深圳怡化电脑股份有限公司 | Equipment module configuration method and device, computer equipment and storage medium |
| CN113822611A (en) * | 2020-07-16 | 2021-12-21 | 北京京东乾石科技有限公司 | Spare part management method and device, computer storage medium and electronic equipment |
| CN112766846A (en) * | 2021-01-11 | 2021-05-07 | 北京航空航天大学 | Spare part transfer network modeling and solving method |
| CN113933542A (en) * | 2021-10-14 | 2022-01-14 | 远景智能国际私人投资有限公司 | Anemometer fault detection method, device, equipment and storage medium |
| CN113933542B (en) * | 2021-10-14 | 2024-01-05 | 远景智能国际私人投资有限公司 | Anemometer fault detection method, device, equipment and storage medium |
| CN115511136A (en) * | 2022-11-01 | 2022-12-23 | 北京磁浮有限公司 | Equipment fault auxiliary diagnosis method and system based on hierarchical analysis and fault tree |
| CN115965140A (en) * | 2022-12-27 | 2023-04-14 | 北京航天智造科技发展有限公司 | Inventory optimal planning method, system, equipment and storage medium |
Also Published As
| Publication number | Publication date |
|---|---|
| CN108876002B (en) | 2021-08-17 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN108876002A (en) | A kind of wind power generating set components standby redundancy inventory's optimization method | |
| Jurasz et al. | Integrating a wind-and solar-powered hybrid to the power system by coupling it with a hydroelectric power station with pumping installation | |
| Sawle et al. | PV-wind hybrid system: A review with case study | |
| Wang et al. | Reliable-economical equilibrium based short-term scheduling towards hybrid hydro-photovoltaic generation systems: Case study from China | |
| Zhang et al. | Capacity configuration optimization of multi-energy system integrating wind turbine/photovoltaic/hydrogen/battery | |
| Mei et al. | Game approaches for hybrid power system planning | |
| CN104242335B (en) | A kind of wind-light storage generator unit capacity configuration optimizing method based on rated capacity | |
| Gonzalez et al. | Optimization of wind farm turbine layout including decision making under risk | |
| Mohammadi et al. | Stochastic scenario-based model and investigating size of battery energy storage and thermal energy storage for micro-grid | |
| Memon et al. | An overview of optimization techniques used for sizing of hybrid renewable energy systems | |
| CN109523060A (en) | Ratio optimization method of the high proportion renewable energy under transmission and distribution network collaboration access | |
| CN109980700A (en) | A kind of distributed generation resource multi-objection optimization planning method, apparatus and equipment | |
| CN104009494A (en) | Environmental economy power generation dispatching method | |
| Pandya et al. | Single-and multiobjective optimal power flow with stochastic wind and solar power plants using moth flame optimization algorithm | |
| CN105305488B (en) | A kind of evaluation method for considering new-energy grid-connected and power transmission network utilization rate being influenced | |
| CN109583655A (en) | A kind of hair transmission of electricity multistage joint Expansion Planning method and system | |
| Liu et al. | Multi-objective generation scheduling towards grid-connected hydro–solar–wind power system based the coordination of economy, management, society, environment: A case study from China | |
| CN113937825A (en) | DG double-layer optimization configuration method based on E-C-Kmeans clustering and SOP optimization | |
| CN107565880B (en) | Optimization-type wind light mutual complementing hybrid power system | |
| CN115759610A (en) | Multi-target planning method for source-grid and storage cooperation of power system and application thereof | |
| Aburiyana et al. | Direct net load forecasting using adaptive neuro fuzzy inference system | |
| Saadaoui et al. | Hybridization and energy storage high efficiency and low cost | |
| CN113536694A (en) | Robust optimization operation method, system and device of comprehensive energy system and storage medium | |
| Kong et al. | Blowing hard is not all we want: Quantity vs quality of wind power in the smart grid | |
| Shendryk et al. | Decision Support System for Efficient Energy Management of MicroGrid with Renewable Energy Sources |
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 |