CN109840858A - A kind of wind power fluctuation clustering method and system based on Gaussian function - Google Patents
A kind of wind power fluctuation clustering method and system based on Gaussian function Download PDFInfo
- Publication number
- CN109840858A CN109840858A CN201711229308.3A CN201711229308A CN109840858A CN 109840858 A CN109840858 A CN 109840858A CN 201711229308 A CN201711229308 A CN 201711229308A CN 109840858 A CN109840858 A CN 109840858A
- Authority
- CN
- China
- Prior art keywords
- wind power
- power
- fluctuation
- sequence
- history
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
Classifications
-
- 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
- Y02E10/70—Wind energy
- Y02E10/76—Power conversion electric or electronic aspects
-
- 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
- Y02E40/00—Technologies for an efficient electrical power generation, transmission or distribution
- Y02E40/70—Smart grids as climate change mitigation technology in the energy generation sector
-
- 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
- Supply And Distribution Of Alternating Current (AREA)
Abstract
The present invention relates to a kind of, and the wind power based on Gaussian function fluctuates clustering method and system, which comprises determines that the wind power of history active power for wind power sequence fluctuates according to the extreme value of history active power for wind power sequence；The fluctuation parameters set of the wind power fluctuation of the history active power for wind power sequence is obtained using Gaussian function；The wind power fluctuation of history active power for wind power sequence is clustered according to the fluctuation parameters set；The application determines that corresponding wind power fluctuates by the history active power for wind power sequence after filtering processing, and the parameter sets based on wind power fluctuation realize the cluster fluctuated to wind power, not only reduce computation complexity, the efficiency of wind power fluctuation cluster is improved, while helping to improve the accuracy that later period wind-powered electricity generation field calculates application.
Description
Technical field
The present invention relates to field of new energy technologies, and in particular to a kind of wind power fluctuation cluster side based on Gaussian function
Method and system.
Background technique
In recent years, the wind-power electricity generation amount of field of new energy generation maintains sustained and rapid growth, and different from conventional power generation, wind-powered electricity generation is by season
The influence of the factors such as section, latitude, height above sea level, Land-sea Distributions, topography and geomorphology has the characteristics that intermittent and fluctuation.With wind-powered electricity generation
Sustainable exploitation utilization and accounting continuous expansion, the efficient consumption of system, balance of electric power and ener and safe and stable operation are got over
Come more difficult.Existing clustering method is mostly cluster based on weather forecast data or model data and then put into wind-force hair
In the calculating application of electrical domain, if wind power prediction calculates, data acquisition difficulty is big in cluster process, and cluster calculation process is multiple
It is miscellaneous, while being difficult to break through the precision bottleneck of wind-powered electricity generation field calculating.
Therefore, the wind power that can accurately embody wind power fluctuation data it is urgent to provide one kind fluctuates clustering method,
To guarantee the stable operation in wind-powered electricity generation field.
Summary of the invention
The present invention provides a kind of wind power fluctuation clustering method and system based on Gaussian function, and the purpose is to utilize height
This function obtains the corresponding wind power fluctuation parameters set of history active power for wind power sequence through being filtered, and is based on wind
Electrical power fluctuation parameters set realizes precisely cluster to wind power fluctuation, thus promote the development of wind-power electricity generation calculating application,
Guarantee the stable and high effective operation of wind-power electricity generation.
The purpose of the present invention is adopt the following technical solutions realization:
A kind of wind power fluctuation clustering method and system based on Gaussian function, it is improved in that the method
Include:
The wind power fluctuation of history active power for wind power sequence is determined according to the extreme value of history active power for wind power sequence；
The fluctuation parameters set of the wind power fluctuation of the history active power for wind power sequence is obtained using Gaussian function；
The wind power fluctuation of history active power for wind power sequence is clustered according to the fluctuation parameters set.
Preferably, the acquisition process of the history active power sequence, comprising:
Burr in history active power data and continuous small is filtered out using the Mallat algorithm that db9 small echo carries out 4 scales
Fluctuation, obtains history active power sequence.
Preferably, the extreme value according to history active power for wind power sequence determines the function of history active power for wind power sequence
Rate fluctuation, comprising:
Power minimum in the power curve of the history active power sequence is increased into power maximum and the function
Rate maximum is reduced to the power curve segment of next power minimum as a power swing.
Preferably, the wave of the wind power fluctuation that the history active power for wind power sequence is obtained using Gaussian function
Dynamic parameter, comprising:
The Gaussian function of wind power fluctuation x is determined as the following formula:
In above formula, a is Extreme Parameters, the amplitude of characterization wind power fluctuation；B is location parameter, and c is variation tendency ginseng
Number, the duration of characterization wind power fluctuation；
The fluctuation parameters aggregate expression is as follows:
In above formula, pnFor the fluctuation parameters set of history active power sequence, n is the wind power wave of the active sequence of history
Dynamic total quantity, akFor the Extreme Parameters of k-th of wind power fluctuation, ckFor the variational trend parameter of k-th of wind power fluctuation, k
∈ (0, n].
Preferably, described that the wind power fluctuation of history active power for wind power sequence is gathered according to fluctuation parameters set
Class, comprising:
The wind-powered electricity generation function is obtained using K-means clustering method according to the Euclidean distance between the fluctuation parameters collection object
The cluster centre of rate fluctuation classifies to the wind power fluctuation of history active power according to the cluster centre.
A kind of wind power fluctuation clustering system based on Gaussian function, it is improved in that the system comprises:
First determining module, for determining history active power for wind power sequence according to the extreme value of history active power for wind power sequence
The wind power of column fluctuates；
Second determining module, for determining the wind power wave of the history active power for wind power sequence using Gaussian function
Dynamic fluctuation parameters set；
Cluster module, for being fluctuated according to wind power of the fluctuation parameters set to history active power for wind power sequence
It is clustered, obtains the cluster centre of the wind power fluctuation.
Preferably, first determining module, comprising:
Acquiring unit, the Mallat algorithm for carrying out 4 scales using db9 small echo filter out in history active power data
Burr and continuous wavelet are dynamic, obtain history active power sequence；
First determining module, comprising:
First determination unit, for power minimum in the power curve of the history active power sequence to be increased to function
Rate maximum and the power maximum are reduced to the power curve segment of next power minimum as a power swing.
Preferably, second determining module, is used for:
The Gaussian function of wind power fluctuation x is determined as the following formula:
In above formula, a is Extreme Parameters, the amplitude of characterization wind power fluctuation；B is location parameter, and c is variation tendency ginseng
Number, the duration of characterization wind power fluctuation；
The fluctuation parameters aggregate expression is as follows:
In above formula, pnFor the fluctuation parameters set of history active power sequence, n is the wind power wave of the active sequence of history
Dynamic total quantity, akFor the Extreme Parameters of k-th of wind power fluctuation, ckFor the variational trend parameter of k-th of wind power fluctuation, k
∈ (0, n].
Preferably, the cluster cell, for utilizing K- according to the Euclidean distance between the fluctuation parameters collection object
Means clustering method obtains the cluster centre of the wind power fluctuation, according to the cluster centre to history active power
Wind power fluctuation is classified.
Compared with prior art, the present invention also has the following beneficial effects:
The technical solution adopted by the present invention determines history wind-powered electricity generation wattful power according to the extreme value of history active power for wind power sequence
The wind power of rate sequence fluctuates；The wind power fluctuation of the history active power for wind power sequence is obtained using Gaussian function
Fluctuation parameters set；The wind power fluctuation of history active power for wind power sequence is gathered according to the fluctuation parameters set
Class obtains the cluster centre of the wind power fluctuation；Above-mentioned technical proposal not only solves this field traditional clustering method number
It is difficult according to obtaining, the defects of computation complexity is high, and realize reasonable cluster fluctuate to wind power, it helps to improve wind-force and sends out
The electrical domain later period calculates the accuracy and efficiency of application, is conducive to the efficient stable operation of wind-power electricity generation.
Detailed description of the invention
Fig. 1 is a kind of flow chart of the wind power fluctuation clustering method based on Gaussian function of the present invention；
Fig. 2 is a kind of wind power fluctuation clustering method filtering front and back power swing data based on Gaussian function of the present invention
Contrast schematic diagram；
Fig. 3 is a kind of structural schematic diagram of the wind power fluctuation clustering system based on Gaussian function of the present invention.
Specific embodiment
It elaborates with reference to the accompanying drawing to a specific embodiment of the invention.
In order to make the object, technical scheme and advantages of the embodiment of the invention clearer, below in conjunction with the embodiment of the present invention
In attached drawing, technical scheme in the embodiment of the invention is clearly and completely described, it is clear that described embodiment is
A part of the embodiment of the present invention, instead of all the embodiments.Based on the embodiments of the present invention, those of ordinary skill in the art
All other embodiment obtained without making creative work, shall fall within the protection scope of the present invention.
The present invention provides a kind of, and the wind power based on Gaussian function fluctuates clustering method and system, is said below
It is bright.
Fig. 1 shows the flow chart of the fluctuation clustering method of the wind power in the embodiment of the present invention based on Gaussian function, such as
Shown in Fig. 1, the method may include:
The wind power fluctuation of history active power for wind power sequence is determined according to the extreme value of history active power for wind power sequence；
The fluctuation parameters set of the wind power fluctuation of the history active power for wind power sequence is obtained using Gaussian function；
The wind power fluctuation of history active power for wind power sequence is clustered according to the fluctuation parameters set.
Wherein, the acquisition process of the history active power sequence may include:
Based on wind power plant history active power data, filtering out history using the Mallat algorithm that db9 small echo carries out 4 scales has
Burr and continuous wavelet in function power data is dynamic, obtains history active power sequence.
Specifically, Mallat algorithm is the fast algorithm of the dyadic wavelet decomposition and reconstruction for a certain function F (t),
Principle, which is equivalent to, constructs certain function space, and signal F (t) is decomposed and carries out certain calculating in function space, obtains you
Then the ingredient wanted reconstructs return original signal again.
Wind power fluctuation clustering method filtering front and back power waves Fig. 2 shows the embodiment of the present invention based on Gaussian function
Dynamic data comparison schematic diagram, as shown in Fig. 2, the filtered wind power curve of cyclical fluctuations smoother stabilization compared with before filtering, goes
The invalid data of application is calculated in addition to influencing data；
Specifically, the extreme value according to history active power for wind power sequence determines the function of history active power for wind power sequence
Rate fluctuates, and may include:
Power minimum in the power curve of the history active power sequence is increased into power maximum and the function
Rate maximum is reduced to the power curve segment of next power minimum as a power swing.
Wherein, the fluctuation of the wind power fluctuation that the history active power for wind power sequence is obtained using Gaussian function
Parameter may include:
The Gaussian function of wind power fluctuation x is determined as the following formula:
In above formula, a is Extreme Parameters, the amplitude of characterization wind power fluctuation；B is location parameter, and it is fixed to be considered as herein
Value；C is variational trend parameter, the duration of characterization wind power fluctuation；
The fluctuation parameters aggregate expression is as follows:
In above formula, pnFor the fluctuation parameters set of history active power sequence, n is the wind power wave of the active sequence of history
Dynamic total quantity, akFor the Extreme Parameters of k-th of wind power fluctuation, ckFor the variational trend parameter of k-th of wind power fluctuation, k
∈ (0, n].
Specifically, described that the wind power fluctuation of history active power for wind power sequence is gathered according to fluctuation parameters set
Class obtains the cluster centre of the wind power fluctuation, may include:
The wind-powered electricity generation function is obtained using K-means clustering method according to the Euclidean distance between the fluctuation parameters collection object
The cluster centre of rate fluctuation classifies to the wind power fluctuation of history active power according to the cluster centre.
Fig. 3 shows the structural schematic diagram of wind power fluctuation clustering system of the embodiment of the present invention based on Gaussian function,
As shown in figure 3, the system may include:
First determining module, for determining history active power for wind power sequence according to the extreme value of history active power for wind power sequence
The wind power of column fluctuates；
Second determining module, for determining the wind power wave of the history active power for wind power sequence using Gaussian function
Dynamic fluctuation parameters set；
Cluster module, for being fluctuated according to wind power of the fluctuation parameters set to history active power for wind power sequence
It is clustered.
Specifically, first determining module may include:
Acquiring unit, the Mallat algorithm for carrying out 4 scales using db9 small echo filter out in history active power data
Burr and continuous wavelet are dynamic, obtain history active power sequence；
First determination unit, for power minimum in the power curve of the history active power sequence to be increased to function
Rate maximum and the power maximum are reduced to the power curve segment of next power minimum as a power swing.
Wherein, second determining module, for determining the Gaussian function of wind power fluctuation x as the following formula:
In above formula, a is Extreme Parameters, the amplitude of characterization wind power fluctuation；B is location parameter, and c is variation tendency ginseng
Number, the duration of characterization wind power fluctuation；
The fluctuation parameters aggregate expression is as follows:
In above formula, pnFor the fluctuation parameters set of history active power sequence, n is the wind power wave of the active sequence of history
Dynamic total quantity, akFor the Extreme Parameters of k-th of wind power fluctuation, ckFor the variational trend parameter of k-th of wind power fluctuation, k
∈ (0, n].
Specifically, the cluster cell, for utilizing K- according to the Euclidean distance between the fluctuation parameters collection object
Means clustering method obtains the cluster centre of the wind power fluctuation, according to the cluster centre to history active power
Wind power fluctuation is classified.
It should be understood by those skilled in the art that, embodiments herein can provide as method, system or computer program
Product.Therefore, complete hardware embodiment, complete software embodiment or reality combining software and hardware aspects can be used in the application
Apply the form of example.Moreover, it wherein includes the computer of computer usable program code that the application, which can be used in one or more,
The computer program implemented in usable storage medium (including but not limited to magnetic disk storage, CD-ROM, optical memory etc.) produces
The form of product.
The application is referring to method, the process of equipment (system) and computer program product according to the embodiment of the present application
Figure and/or block diagram describe.It should be understood that every one stream in flowchart and/or the block diagram can be realized by computer program instructions
The combination of process and/or box in journey and/or box and flowchart and/or the block diagram.It can provide these computer programs
Instruct the processor of general purpose computer, special purpose computer, Embedded Processor or other programmable data processing devices to produce
A raw machine, so that being generated by the instruction that computer or the processor of other programmable data processing devices execute for real
The device for the function of being specified in present one or more flows of the flowchart and/or one or more blocks of the block diagram.
These computer program instructions, which may also be stored in, is able to guide computer or other programmable data processing devices with spy
Determine in the computer-readable memory that mode works, so that it includes referring to that instruction stored in the computer readable memory, which generates,
Enable the manufacture of device, the command device realize in one box of one or more flows of the flowchart and/or block diagram or
The function of being specified in multiple boxes.
These computer program instructions also can be loaded onto a computer or other programmable data processing device, so that counting
Series of operation steps are executed on calculation machine or other programmable devices to generate computer implemented processing, thus in computer or
The instruction executed on other programmable devices is provided for realizing in one or more flows of the flowchart and/or block diagram one
The step of function of being specified in a box or multiple boxes.
Finally it should be noted that: the above embodiments are merely illustrative of the technical scheme of the present invention and are not intended to be limiting thereof, to the greatest extent
Invention is explained in detail referring to above-described embodiment for pipe, it should be understood by those ordinary skilled in the art that: still
It can be with modifications or equivalent substitutions are made to specific embodiments of the invention, and without departing from any of spirit and scope of the invention
Modification or equivalent replacement, should all cover within the scope of the claims of the present invention.
Claims (10)
1. a kind of wind power based on Gaussian function fluctuates clustering method, which is characterized in that the described method includes:
The wind power fluctuation of history active power for wind power sequence is determined according to the extreme value of history active power for wind power sequence；
The fluctuation parameters set of the wind power fluctuation of the history active power for wind power sequence is obtained using Gaussian function；
The wind power fluctuation of history active power for wind power sequence is clustered according to the fluctuation parameters set.
2. the method as described in claim 1, which is characterized in that the acquisition process of the history active power sequence, comprising:
Burr in history active power data is filtered out using the Mallat algorithm that db9 small echo carries out 4 scales and continuous wavelet is dynamic,
Obtain history active power sequence.
3. the method as described in claim 1, which is characterized in that described to be determined according to the extreme value of history active power for wind power sequence
The power swing of history active power for wind power sequence, comprising:
Power minimum in the power curve of the history active power sequence is increased into power maximum and the power pole
Big value is reduced to the power curve segment of next power minimum as a power swing.
4. the method as described in claim 1, which is characterized in that described to obtain the history wind-powered electricity generation wattful power using Gaussian function
The fluctuation parameters of the wind power fluctuation of rate sequence, comprising:
The Gaussian function of wind power fluctuation x is determined as the following formula:
In above formula, a is Extreme Parameters, the amplitude of characterization wind power fluctuation；B is location parameter, and c is variational trend parameter, table
Levy the duration of wind power fluctuation；
The fluctuation parameters aggregate expression is as follows:
In above formula, pnFor the fluctuation parameters set of history active power sequence, n is that the wind power fluctuation of the active sequence of history is total
Quantity, akFor the Extreme Parameters of k-th of wind power fluctuation, ckFor the variational trend parameter of k-th of wind power fluctuation, k ∈
(0,n]。
5. the method as described in claim 1, which is characterized in that it is described according to fluctuation parameters set to history active power for wind power
The wind power fluctuation of sequence is clustered, comprising:
The wind power wave is obtained using K-means clustering method according to the Euclidean distance between the fluctuation parameters collection object
Dynamic cluster centre classifies to the wind power fluctuation of history active power according to the cluster centre.
6. a kind of wind power based on Gaussian function fluctuates clustering system, which is characterized in that the system comprises:
First determining module, for determining history active power for wind power sequence according to the extreme value of history active power for wind power sequence
Wind power fluctuation；
Second determining module, for determining what the wind power of the history active power for wind power sequence fluctuated using Gaussian function
Fluctuation parameters set；
Cluster module is carried out for being fluctuated according to wind power of the fluctuation parameters set to history active power for wind power sequence
Cluster.
7. system as claimed in claim 6, which is characterized in that first determining module, comprising:
Acquiring unit, the Mallat algorithm for carrying out 4 scales using db9 small echo filter out the burr in history active power data
It is dynamic with continuous wavelet, obtain history active power sequence.
8. system as claimed in claim 6, which is characterized in that first determining module, comprising:
First determination unit, for power minimum in the power curve of the history active power sequence to be increased to power pole
Big value and the power maximum are reduced to the power curve segment of next power minimum as a power swing.
9. system as claimed in claim 6, which is characterized in that second determining module is used for:
The Gaussian function of wind power fluctuation x is determined as the following formula:
In above formula, a is Extreme Parameters, the amplitude of characterization wind power fluctuation；B is location parameter, and c is variational trend parameter, table
Levy the duration of wind power fluctuation；
The fluctuation parameters aggregate expression is as follows:
In above formula, pnFor the fluctuation parameters set of history active power sequence, n is that the wind power fluctuation of the active sequence of history is total
Quantity, akFor the Extreme Parameters of k-th of wind power fluctuation, ckFor the variational trend parameter of k-th of wind power fluctuation, k ∈
(0,n]。
10. system as claimed in claim 6, which is characterized in that the cluster cell, for according to the fluctuation parameters set
Euclidean distance between object obtains the cluster centre of the wind power fluctuation using K-means clustering method, according to described poly-
Classify to the wind power fluctuation of history active power at class center.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201711229308.3A CN109840858A (en) | 2017-11-29 | 2017-11-29 | A kind of wind power fluctuation clustering method and system based on Gaussian function |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201711229308.3A CN109840858A (en) | 2017-11-29 | 2017-11-29 | A kind of wind power fluctuation clustering method and system based on Gaussian function |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN109840858A true CN109840858A (en) | 2019-06-04 |
Family
ID=66882452
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201711229308.3A Pending CN109840858A (en) | 2017-11-29 | 2017-11-29 | A kind of wind power fluctuation clustering method and system based on Gaussian function |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN109840858A (en) |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111130117A (en) * | 2020-01-07 | 2020-05-08 | 杭州电子科技大学 | Probability optimal power flow calculation method based on high-dimensional data clustering |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104182914A (en) * | 2014-09-05 | 2014-12-03 | 国家电网公司 | Wind power output time series modeling method based on fluctuation characteristics |
| US20150302313A1 (en) * | 2014-04-22 | 2015-10-22 | State Grid Corporation Of China | Method of predicating ultra-short-term wind power based on self-learning composite data source |
| CN105701590A (en) * | 2014-11-28 | 2016-06-22 | 国家电网公司 | Wind power fluctuation probability distribution description method based on great likelihood estimation |
| CN105939026A (en) * | 2016-05-27 | 2016-09-14 | 电子科技大学 | Hybrid Laplace distribution-based wind power fluctuation quantity probability distribution model building method |
| CN107358060A (en) * | 2017-09-06 | 2017-11-17 | 大连理工大学 | A kind of method estimated wind power prediction error burst based on HMM |
-
2017
- 2017-11-29 CN CN201711229308.3A patent/CN109840858A/en active Pending
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20150302313A1 (en) * | 2014-04-22 | 2015-10-22 | State Grid Corporation Of China | Method of predicating ultra-short-term wind power based on self-learning composite data source |
| CN104182914A (en) * | 2014-09-05 | 2014-12-03 | 国家电网公司 | Wind power output time series modeling method based on fluctuation characteristics |
| CN105701590A (en) * | 2014-11-28 | 2016-06-22 | 国家电网公司 | Wind power fluctuation probability distribution description method based on great likelihood estimation |
| CN105939026A (en) * | 2016-05-27 | 2016-09-14 | 电子科技大学 | Hybrid Laplace distribution-based wind power fluctuation quantity probability distribution model building method |
| CN107358060A (en) * | 2017-09-06 | 2017-11-17 | 大连理工大学 | A kind of method estimated wind power prediction error burst based on HMM |
Non-Patent Citations (2)
| Title |
|---|
| 李驰: "基于波动特性的新能源出力时间序列建模方法研究", 中国优秀硕士学位论文全文数据库，工程科技II辑 * |
| 沈超 等: "基于概率统计的风电出力时间序列生成方法", 电力科学与技术学报 * |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111130117A (en) * | 2020-01-07 | 2020-05-08 | 杭州电子科技大学 | Probability optimal power flow calculation method based on high-dimensional data clustering |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN107609697B (en) | A kind of Wind power forecasting method | |
| Yang et al. | Probabilistic short-term wind power forecast using componential sparse Bayesian learning | |
| CN109524993A (en) | The typical week power output scene generating method of wind-powered electricity generation photovoltaic for Mid-long Term Optimized Scheduling | |
| CN112183641B (en) | Transient frequency stability assessment method and system integrating prediction-correction deep learning | |
| Li et al. | Dispatch planning of a wide-area wind power-energy storage scheme based on ensemble empirical mode decomposition technique | |
| CN108428017A (en) | Wind power interval prediction method based on core extreme learning machine quantile estimate | |
| CN103065202A (en) | Wind power plant ultrashort term wind speed prediction method based on combination kernel function | |
| CN109002926A (en) | The photovoltaic power generation quantity prediction model and its construction method of a kind of high accuracy and application | |
| CN108009689A (en) | The training of wind speed correction model, wind speed modification method, device and wind power generating set | |
| CN102938562A (en) | Prediction method of total wind electricity power in area | |
| CN102542133A (en) | Short-time wind speed forecasting method and system for wind power plant | |
| CN105046044A (en) | Non-stationary wind speed prediction method based on optimum wavelet-packet transformation | |
| CN110739726A (en) | multi-type power supply capacity long-term planning method considering offshore wind power access | |
| CN111915079A (en) | Hybrid KNN wind power prediction method and system | |
| CN109840858A (en) | A kind of wind power fluctuation clustering method and system based on Gaussian function | |
| CN104700156B (en) | A kind of wind power forecasting method based on support vector machines selection variables | |
| CN105528517B (en) | Based on neural network, wavelet decomposition predicting power of photovoltaic plant method, system | |
| CN109993335A (en) | A kind of probability forecast method and system for wind power | |
| CN116578855A (en) | Electric energy metering method, system, equipment and medium of alternating-current micro-grid | |
| CN114006392B (en) | Energy storage node capacity demand estimation system of distributed power system | |
| CN106548248A (en) | A kind of harmonic trend probability index Forecasting Methodology of the electrical network of power station containing large-sized photovoltaic | |
| CN108365614A (en) | A kind of regional power grid oscillation on-line early warning method and device | |
| CN110210657A (en) | A kind of power of fan prediction technique, system and computer storage medium based on single machine model | |
| Dong et al. | An integrated ultra short term power forecasting method for regional wind–pv–hydro | |
| Cui et al. | Short term power forecasting of a wind farm based on atomic sparse decomposition theory |
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 |