CN116707035A - Active power control method depending on low wind speed dynamic programming - Google Patents
Active power control method depending on low wind speed dynamic programming Download PDFInfo
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J3/00—Circuit arrangements for ac mains or ac distribution networks
- H02J3/38—Arrangements for parallely feeding a single network by two or more generators, converters or transformers
- H02J3/46—Controlling of the sharing of output between the generators, converters, or transformers
- H02J3/48—Controlling the sharing of the in-phase component
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J3/00—Circuit arrangements for ac mains or ac distribution networks
- H02J3/38—Arrangements for parallely feeding a single network by two or more generators, converters or transformers
- H02J3/381—Dispersed generators
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J2203/00—Indexing scheme relating to details of circuit arrangements for AC mains or AC distribution networks
- H02J2203/10—Power transmission or distribution systems management focussing at grid-level, e.g. load flow analysis, node profile computation, meshed network optimisation, active network management or spinning reserve management
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- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J2203/00—Indexing scheme relating to details of circuit arrangements for AC mains or AC distribution networks
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- H—ELECTRICITY
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- H02J2300/00—Systems for supplying or distributing electric power characterised by decentralized, dispersed, or local generation
- H02J2300/20—The dispersed energy generation being of renewable origin
- H02J2300/28—The renewable source being wind energy
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- 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
- Y02E10/72—Wind turbines with rotation axis in wind direction
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Abstract
The invention belongs to the technical field of wind power generation, and particularly relates to an active power control method depending on low wind speed dynamic programming. According to the wind energy capturing method, the historical wind speed parameters under a plurality of monitoring periods can be evaluated and analyzed, so that a primary wind speed fluctuation node is obtained, a secondary wind speed fluctuation node is determined by judging the duration of the primary wind speed fluctuation node, the executable duration and the non-executable duration are determined based on the secondary wind speed fluctuation node, and then the selection of the wind energy capturing model is determined by evaluating the concentrated bias of the non-executable duration, so that different wind energy capturing models can be selected under the environment without ventilation speed, the conversion efficiency of wind energy in the wind power generation process can be ensured, and meanwhile, the power consumption condition of a user can be ensured not to be influenced.
Description
Technical Field
The invention belongs to the technical field of wind power generation, and particularly relates to an active power control method depending on low wind speed dynamic programming.
Background
The permeability of wind power generation in a power system is continuously improved, no matter what energy is used for generating, the generator is expected to stably work near the maximum power point, the same requirement is also met for the wind power generation system, and an important design of the wind turbine is to absorb and convert wind energy to the maximum extent. For the wind turbine, the efficiency improvement of 1% is very difficult, and great economic benefit can be brought. The advent of variable speed wind turbines has provided a technological approach to maximize wind energy capture. Compared with a constant-speed wind turbine, the variable-speed wind turbine can adjust the running rotating speed according to the real-time wind speed change, so that the wind energy capturing efficiency is improved, the structural load is reduced, and the variable-speed wind turbine is widely applied, in particular to a low-wind-speed wind generating set with relatively low generating efficiency. However, the output power of the wind power generation system is affected not only by the system load, but also by the wind intensity and changes of the external environment. In order to improve the power generation efficiency of the wind power generation system, it is necessary to track the maximum power point in consideration of the load and the external environment.
In the prior art, there are many methods for controlling active power, such as a tip speed ratio method, an optimal torque method, a hill climbing search method, etc., the adaptive scenes are different, in real life, wind speed is an uncontrollable factor, and a single control method is consistently adopted to reduce wind energy conversion efficiency in stages, so that not only is the user demand not met, but also the benefits brought by wind power generation are influenced.
Disclosure of Invention
The invention aims to provide an active power control method which depends on low wind speed dynamic programming, wherein the centralized deflection of the active power control method can be determined according to fluctuation of wind speed, and then wind energy capturing model switching is realized according to the centralized deflection.
The technical scheme adopted by the invention is as follows:
an active power control method relying on low wind speed dynamic planning, comprising:
acquiring low wind speed information, wherein the low wind speed information comprises a historical wind speed parameter and a current wind speed parameter;
constructing a plurality of monitoring time periods, setting a plurality of sampling nodes in the monitoring time periods respectively, and summarizing historical wind speed parameters under the sampling nodes into a plurality of data sets to be evaluated;
respectively inputting the historical wind speed parameters in a plurality of monitoring periods into an evaluation model to obtain a plurality of primary wind speed fluctuation nodes;
acquiring the duration time between adjacent first-level wind speed fluctuation nodes, and calibrating the duration time as the duration time to be checked;
acquiring a rated time length and comparing the rated time length with the time length to be checked;
if the time length to be checked is smaller than the rated time length, indicating that the primary wind speed fluctuation node with the next back is an instantaneous node, and continuously comparing with the primary wind speed fluctuation node with the next primary;
if the time length to be checked is greater than or equal to the rated time length, indicating that the primary wind speed fluctuation node is a non-transient node, and calibrating the primary wind speed fluctuation node as a secondary wind speed fluctuation node;
taking adjacent three secondary wind speed fluctuation nodes as a group, and inputting the group into an optimization model to obtain a segmentation duration;
acquiring the segment duration under each monitoring period, and inputting the segment duration into a classification model to obtain executable duration and non-executable duration;
and acquiring the historical wind speed parameter under the non-executable duration, measuring and calculating the average value of the historical wind speed parameter to obtain the average wind speed, inputting the average wind speed into a planning model, and matching the average wind speed with a corresponding wind energy capturing model.
In a preferred embodiment, the historical wind speed parameter and the current wind speed parameter each include constant wind, gust wind, and gradual wind.
In a preferred embodiment, the step of inputting the historical wind speed parameters in the monitoring periods into the evaluation model to obtain the first-level wind speed fluctuation nodes includes:
acquiring fluctuation quantity of the historical wind speed parameters under adjacent sampling nodes from the data set to be evaluated, and calibrating the fluctuation quantity as the parameters to be checked;
invoking an evaluation function from the evaluation model;
acquiring rated wind speed fluctuation quantity, and inputting the rated wind speed fluctuation quantity and the parameter to be checked into an evaluation function together to obtain wind speed fluctuation deviation quantity;
acquiring an allowable floating threshold value and comparing the allowable floating threshold value with the fluctuation deviation amount of the wind speed;
if the fluctuation deviation of the wind speed is smaller than the allowable floating threshold, judging that the historical wind speed parameter is not fluctuated, and continuously comparing the historical wind speed parameter of the next level;
if the fluctuation deviation of the wind speed is greater than or equal to the allowable floating threshold, judging the fluctuation of the historical wind speed parameter, and calibrating a sampling node corresponding to the fluctuation of the wind speed parameter as a primary wind speed fluctuation node.
In a preferred scheme, the step of inputting a group of three adjacent secondary wind speed fluctuation nodes into an optimization model to obtain the segment duration includes:
acquiring adjacent three secondary wind speed fluctuation nodes, and respectively calibrating the adjacent three secondary wind speed fluctuation nodes as a first fluctuation node, a second fluctuation node and a third fluctuation node;
acquiring the time length between the first fluctuation node and the second fluctuation node and the time length between the second fluctuation node and the third fluctuation node, and calibrating the time length as a first time length to be optimized and a second time length to be optimized respectively;
calling an optimization function from the optimization model, and inputting the first time length to be optimized and the second time length to be optimized into the optimization function to obtain segmented nodes;
and determining the segment duration from the time period between the adjacent segment nodes.
In a preferred embodiment, the step of obtaining the segment duration under each monitoring period and inputting the segment duration into the classification model to obtain the executable duration and the non-executable duration includes:
acquiring the segmentation duration under the monitoring period;
calling a classification interval from the classification model, respectively comparing the classification interval with the segmentation duration, and judging whether the segmentation duration belongs to the classification interval or not;
if yes, judging the segmentation duration to be non-executable duration;
if not, judging the segmentation duration as executable duration.
In a preferred embodiment, the step of obtaining the executable duration and the non-executable duration includes:
acquiring the number of all non-executable time periods, and calibrating the number as a parameter to be compared;
calculating the occupation ratio of the parameters to be compared in the total executable time length and the total non-executable time length;
acquiring an evaluation threshold value and comparing the evaluation threshold value with the occupation ratio of the parameters to be compared;
if the occupation ratio of the parameters to be compared is larger than or equal to an evaluation threshold, inputting the non-executable time length in the monitoring period into a bias evaluation model to obtain a variation trend of the non-executable time length;
and if the occupation ratio of the parameters to be compared is smaller than an evaluation threshold value, indicating that the non-executable duration in the monitoring period is an instantaneous phenomenon, and not inputting the non-executable duration into a bias evaluation model.
In a preferred embodiment, the step of inputting the non-executable time length in the monitoring period into the bias evaluation model to obtain the variation trend of the non-executable time length includes:
acquiring the number of non-executable time durations in each monitoring period, and calibrating the number as a parameter to be evaluated;
invoking an evaluation function from the bias evaluation model;
inputting the parameters to be evaluated into an evaluation function, and calibrating the output result as a centralized deviation;
if the value of the centralized deviation is larger than zero, the non-executable duration is gradually reduced;
if the value of the centralized deviation is equal to zero, the non-executable duration is indicated to be evenly distributed in a plurality of monitoring periods;
and if the value of the centralized deviation is smaller than zero, the non-executable duration is gradually increased.
The invention also provides an active power control system depending on the low wind speed dynamic programming, which is applied to the active power control method depending on the low wind speed dynamic programming, and comprises the following steps:
the system comprises a first acquisition module, a second acquisition module and a control module, wherein the first acquisition module is used for acquiring low wind speed information, and the low wind speed information comprises a historical wind speed parameter and a current wind speed parameter;
the sampling module is used for constructing a plurality of monitoring time periods, setting a plurality of sampling nodes in the monitoring time periods respectively, and summarizing the historical wind speed parameters under the sampling nodes into a plurality of data sets to be evaluated;
the evaluation module is used for respectively inputting the historical wind speed parameters in a plurality of monitoring periods into an evaluation model to obtain a plurality of primary wind speed fluctuation nodes;
the second acquisition module is used for acquiring the duration between the adjacent first-level wind speed fluctuation nodes and calibrating the duration as the duration to be checked;
the comparison module is used for obtaining rated time length and comparing the rated time length with the time length to be checked;
if the time length to be checked is smaller than the rated time length, indicating that the primary wind speed fluctuation node with the next back is an instantaneous node, and continuously comparing with the primary wind speed fluctuation node with the next primary;
if the time length to be checked is greater than or equal to the rated time length, indicating that the primary wind speed fluctuation node is a non-transient node, and calibrating the primary wind speed fluctuation node as a secondary wind speed fluctuation node;
the optimizing module is used for taking adjacent three secondary wind speed fluctuation nodes as a group, and inputting the group of adjacent three secondary wind speed fluctuation nodes into an optimizing model to obtain the segmentation duration;
the classification module is used for acquiring the segment duration under each monitoring period and inputting the segment duration into the classification model to obtain the executable duration and the non-executable duration;
and the planning module is used for acquiring the historical wind speed parameters under the non-executable duration, measuring and calculating the average value of the historical wind speed parameters to obtain the average wind speed, and planning a corresponding wind energy capturing model according to the average wind speed.
In a preferred embodiment, the wind energy capturing model includes a first measuring unit, a second measuring unit, and a third measuring unit, where the first measuring unit, the second measuring unit, and the third measuring unit respectively correspond to an average wind speed lower limit value.
And an active power control terminal relying on dynamic planning of low wind speeds, comprising:
at least one processor;
and a memory communicatively coupled to the at least one processor;
wherein the memory stores a computer program executable by the at least one processor to enable the at least one processor to perform the active power control method dependent on low wind speed dynamic programming described above.
The invention has the technical effects that:
according to the wind energy capturing method, the historical wind speed parameters under a plurality of monitoring periods can be evaluated and analyzed, so that a primary wind speed fluctuation node is obtained, a secondary wind speed fluctuation node is determined by judging the duration of the primary wind speed fluctuation node, the executable duration and the non-executable duration are determined based on the secondary wind speed fluctuation node, and then the selection of the wind energy capturing model is determined by evaluating the concentrated bias of the non-executable duration, so that different wind energy capturing models can be selected under the environment without ventilation speed, the conversion efficiency of wind energy in the wind power generation process can be ensured, and meanwhile, the power consumption condition of a user can be ensured not to be influenced.
Drawings
FIG. 1 is a flow chart of a method provided by the present invention;
fig. 2 is a block diagram of a system provided by the present invention.
Detailed Description
In order that the above-recited objects, features and advantages of the present invention will become more readily apparent, a more particular description of the invention will be rendered by reference to specific embodiments thereof which are illustrated in the appended drawings.
In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present invention, but the present invention may be practiced in other ways other than those described herein, and persons skilled in the art will readily appreciate that the present invention is not limited to the specific embodiments disclosed below.
Further, reference herein to "one embodiment" or "an embodiment" means that a particular feature, structure, or characteristic can be included in at least one implementation of the invention. The appearances of the phrase "in one preferred embodiment" in various places in the specification are not necessarily all referring to the same embodiment, nor are separate or alternative embodiments mutually exclusive of other embodiments.
Referring to fig. 1 and 2, the present invention provides an active power control method relying on dynamic planning of low wind speed, comprising:
s1, acquiring low wind speed information, wherein the low wind speed information comprises a historical wind speed parameter and a current wind speed parameter, and the historical wind speed parameter and the current wind speed parameter comprise constant wind, gusts and gradual change wind;
s2, constructing a plurality of monitoring time periods, respectively setting a plurality of sampling nodes in the monitoring time periods, and summarizing historical wind speed parameters under the sampling nodes into a plurality of data sets to be evaluated;
s3, respectively inputting historical wind speed parameters in a plurality of monitoring periods into an evaluation model to obtain a plurality of first-stage wind speed fluctuation nodes;
s4, acquiring the duration time between adjacent first-stage wind speed fluctuation nodes, and calibrating the duration time as the duration time to be checked;
s5, acquiring a rated time length and comparing the rated time length with a time length to be checked;
if the time length to be checked is smaller than the rated time length, indicating that the primary wind speed fluctuation node with the next rank is an instantaneous node, and continuously comparing with the primary wind speed fluctuation node with the next rank;
if the time length to be checked is longer than or equal to the rated time length, indicating that the primary wind speed fluctuation node is a non-transient node, and calibrating the primary wind speed fluctuation node as a secondary wind speed fluctuation node;
s6, taking adjacent three secondary wind speed fluctuation nodes as a group, and inputting the group into an optimization model to obtain the segmentation duration;
s7, acquiring the segment duration under each monitoring period, and inputting the segment duration into a classification model to obtain executable duration and non-executable duration;
s8, acquiring a historical wind speed parameter under the non-executable duration, measuring and calculating an average value of the historical wind speed parameter to obtain an average wind speed, inputting the average wind speed into a planning model, and matching a corresponding wind energy capturing model.
As described in the above steps S1-S8, along with the increase of environmental awareness, the traditional power generation industry is not limited to thermal power generation, more solar power generation and wind power generation, for wind power, the solar power generation and the wind power generation are inexhaustible energy sources in nature, in order to ensure the conversion efficiency of wind energy in the wind power generation process, corresponding wind turbines are configured to absorb and convert wind energy to the greatest extent, however, the change of wind speed is non-manpower controllable, so that, for different situations, the maximum power point tracking strategies of multiple wind power generating sets are derived, even in the same region, different wind speeds exist in different time periods, but in the conventional application, only one tracking strategy is adopted to control the output power of the generating sets, in this embodiment, the low wind speed information is firstly obtained, and then multiple monitoring periods are constructed, setting a plurality of sampling nodes in a monitoring period, counting historical wind speed parameters under the sampling nodes, inputting the historical wind speed parameters into an evaluation model to obtain a first-stage wind speed fluctuation node, determining the time length to be checked according to the first-stage wind speed fluctuation node, and comparing the time length to be checked with a rated time length, wherein the rated time length is required to be set according to actual requirements because the wind power generation effect is related to wind field scale, fan model, tower wheel hub height and the like, the specific limitation and redundancy are not required, the secondary wind speed fluctuation node is determined according to the comparison result, the secondary wind speed fluctuation node is input into an optimization model to obtain a sectional time length, and the sectional time length is input into a classification model to determine executable time length and non-executable time length, and under the executable time length, the generator set is controlled according to the original tracking strategy, while the non-executable time length is input into the planning model, the corresponding wind energy capturing model is matched, and the tracking strategy is adjusted, so that the wind energy conversion efficiency can be correspondingly improved.
In a preferred embodiment, the step of inputting the historical wind speed parameters in the plurality of monitoring periods into the evaluation model to obtain a plurality of first-stage wind speed fluctuation nodes includes:
s301, acquiring fluctuation quantity of a historical wind speed parameter under an adjacent sampling node from a data set to be evaluated, and calibrating the fluctuation quantity as a parameter to be checked;
s302, calling an evaluation function from the evaluation model;
s303, acquiring rated wind speed fluctuation quantity, and inputting the rated wind speed fluctuation quantity and parameters to be checked into an evaluation function together to obtain wind speed fluctuation deviation quantity;
s304, acquiring an allowable floating threshold value and comparing the allowable floating threshold value with the fluctuation deviation amount of the wind speed;
if the fluctuation deviation amount of the wind speed is smaller than the allowable floating threshold value, judging that the historical wind speed parameter does not fluctuate, and continuously comparing the historical wind speed parameter of the next level;
if the fluctuation deviation of the wind speed is greater than or equal to the allowable floating threshold, the fluctuation of the historical wind speed parameter is judged, and the sampling node corresponding to the fluctuation deviation of the wind speed parameter is calibrated as a primary wind speed fluctuation node.
As described in the above steps S301 to S304, after the data set to be evaluated is determined, the fluctuation amount of the historical wind speed parameter under the adjacent sampling node is determined as the parameter to be verified, and is input into the evaluation model, and then the evaluation function is called from the evaluation model, where the evaluation function is:wherein->Represents the fluctuation deviation of wind speed, +.>Indicating rated wind speed fluctuation amount->And the parameter to be verified is represented, and then the parameter to be verified is compared with an allowable floating threshold according to the measuring and calculating result of the evaluation function, so that whether the historical wind speed parameter fluctuates or not is judged, and after the historical wind speed parameter is determined to fluctuate, the corresponding sampling node is calibrated to be a primary wind speed fluctuation node, so that corresponding data support is provided for the execution of a follow-up optimization model.
In a preferred embodiment, the step of inputting a group of three adjacent secondary wind speed fluctuation nodes into the optimization model to obtain the segment duration includes:
s601, acquiring adjacent three secondary wind speed fluctuation nodes, and respectively calibrating the adjacent three secondary wind speed fluctuation nodes as a first fluctuation node, a second fluctuation node and a third fluctuation node;
s602, acquiring time lengths between a first fluctuation node and a second fluctuation node and time lengths between the second fluctuation node and a third fluctuation node, and calibrating the time lengths as a first time length to be optimized and a second time length to be optimized respectively;
s603, calling an optimization function from the optimization model, and inputting a first time length to be optimized and a second time length to be optimized into the optimization function to obtain a segment node;
s604, determining the segment duration of the time period between adjacent segment nodes.
As described in the above steps S601-S604, after all the primary wind speed fluctuation nodes are determined, the time length between the adjacent primary wind speed fluctuation nodes is counted and determined as the time length to be checked, and then the time length to be checked is compared with the rated time length, where the rated time length is set to determine whether the time length to be checked can meet the requirement of the switching tracking policy, for example, the time length to be checked is only 5min, and the time length is very short, then there is no obvious influence on wind power generation, and if the time length to be checked is 1h, then there is obvious influence on the efficiency of wind power generation, where the rated time length needs to be set according to specific conditions, and no explicit limitation is imposed here, if the time length to be checked is smaller than the rated time length, determining a primary wind speed fluctuation node with a corresponding position behind the primary wind speed fluctuation node as an instantaneous node, otherwise, calibrating the primary wind speed fluctuation node as a secondary wind speed fluctuation node, and after the secondary wind speed fluctuation node is determined, firstly arranging the secondary wind speed fluctuation node according to the sequence of occurrence time, and executing optimization operation by taking adjacent three secondary wind speed fluctuation nodes as a group, wherein for convenience of distinguishing, the embodiment sequentially calibrates the secondary wind speed fluctuation node as a first fluctuation node, a second fluctuation node and a third fluctuation node according to the position, and calibrates the time interval between the first fluctuation node, the second fluctuation node and the third fluctuation node as a first time to be optimized and a second time to be optimized respectively, and then inputs the first time to be optimized and the second time to be optimized and the corresponding historical wind speed parameters thereof into an optimization function, wherein the expression of the optimization function is as follows:wherein->And->Respectively representing a first duration to be optimized and a second duration to be optimized, < > in->Representing the number of historical wind speed parameters between the first and second undulating nodes,representing the number of historical wind speed parameters between the second and third fluctuating nodes, +.>Representing a historical wind speed parameter between a first fluctuating node and a second fluctuating node,/>Respectively representing the historical wind speed parameter between the second fluctuation node and the third fluctuation node,/and the wind speed parameter>The specific value of the wind speed deviation evaluation interval can be set according to actual requirements, and the specific value is used for judging whether the historical wind speed parameter under the second time length to be optimized is close to the historical wind speed parameter under the first time length to be optimized, if the historical wind speed parameter under the second time length to be optimized is satisfied, the second fluctuation node is screened out, the first fluctuation node and the third fluctuation node are determined to be sectional nodes, otherwise, the first fluctuation node, the second fluctuation node and the third fluctuation node are determined to be sectional nodes, and finally the sectional time length is determined by the adjacent sectional nodes.
In a preferred embodiment, the step of obtaining the segment duration under each monitoring period and inputting the segment duration into the classification model to obtain the executable duration and the non-executable duration includes:
s701, acquiring a segmentation duration in a monitoring period;
s702, calling a classification interval from the classification model, respectively comparing the classification interval with the segmentation duration, and judging whether the segmentation duration belongs to the classification interval or not;
if yes, judging that the segmentation duration is non-executable duration;
if not, judging the segmentation duration as the executable duration.
As described in the above steps S701-S702, after determining the segment duration, the segment duration is compared with the classification interval, the classification interval includes an executable interval and a non-executable interval, and the classification interval is determined according to the historical profits of implementing different tracking strategies under each segment duration, and for the executable interval corresponding to the positive historical profits and for the non-executable interval corresponding to the negative historical profits, the executable performance of each subsequent segment duration can be determined based on the corresponding executable interval, so that the profits of wind power generation are not affected under the condition of ensuring that the wind speed fluctuates.
In a preferred embodiment, the steps after obtaining the executable duration and the non-executable duration include:
stp1, obtaining the number of all non-executable time periods, and calibrating the number as a parameter to be compared;
stp2, calculating the occupation ratio of the parameters to be compared in the total amount of the executable time and the non-executable time;
stp3, acquiring an evaluation threshold value, and comparing the evaluation threshold value with the occupation ratio of the parameters to be compared;
if the ratio of the parameters to be compared is greater than or equal to the evaluation threshold, inputting the non-executable time length in the monitoring period into a bias evaluation model to obtain the variation trend of the non-executable time length;
if the ratio of the parameters to be compared is smaller than the evaluation threshold, the non-executable duration in the monitoring period is an instantaneous phenomenon, and the non-executable duration is not input into the bias evaluation model.
As described in the above steps Stp1-Stp3, due to the inconsistent climate in different regions, the number of non-executable time periods may occupy a relatively large amount, not only the wind energy conversion rate is reduced, but also the trend of negative gain growth is further enlarged without adjusting the tracking strategy.
In a preferred embodiment, the step of inputting the non-executable time duration in the monitoring period into the bias evaluation model to obtain the variation trend of the non-executable time duration includes:
stp301, obtaining the number of non-executable time periods in each monitoring period, and calibrating the number as a parameter to be evaluated;
stp302, calling an evaluation function from the bias evaluation model;
stp303, input the parameter to be evaluated into the evaluation function, and calibrate its output result as the centralized bias;
if the value of the centralized deviation is larger than zero, the non-executable duration is gradually reduced;
if the value of the centralized skewness is equal to zero, the non-executable duration is indicated to be evenly distributed in a plurality of monitoring periods;
if the value of the centralized skewness is smaller than zero, the non-executable duration is gradually increased.
As described in the above steps Stp301 to Stp303, when the duty ratio of the non-executable time period is higher than the evaluation threshold, the evaluation function is set as:wherein->Indicating the concentration bias of the parameter to be evaluated, < ->Representing the number of parameters to be evaluated, +.>Representing the parameters to be evaluated->Representing the mean value of the parameter to be evaluated so thatAnd (3) evaluating the centralized bias of the parameters to be evaluated, determining a centralized distribution area according to an evaluation result, and then adjusting a tracking strategy according to the wind speed parameters in the distribution area.
The invention also provides an active power control system depending on the low wind speed dynamic programming, which is applied to the active power control method depending on the low wind speed dynamic programming, and comprises the following steps:
the system comprises a first acquisition module, a second acquisition module and a control module, wherein the first acquisition module is used for acquiring low wind speed information, and the low wind speed information comprises a historical wind speed parameter and a current wind speed parameter;
the sampling module is used for constructing a plurality of monitoring time periods, setting a plurality of sampling nodes in the monitoring time periods respectively, and summarizing the historical wind speed parameters under the sampling nodes into a plurality of data sets to be evaluated;
the evaluation module is used for respectively inputting the historical wind speed parameters in a plurality of monitoring periods into the evaluation model to obtain a plurality of first-stage wind speed fluctuation nodes;
the second acquisition module is used for acquiring the duration between the adjacent first-stage wind speed fluctuation nodes and calibrating the duration as the duration to be checked;
the comparison module is used for acquiring rated time length and comparing the rated time length with the time length to be checked;
if the time length to be checked is smaller than the rated time length, indicating that the primary wind speed fluctuation node with the next rank is an instantaneous node, and continuously comparing with the primary wind speed fluctuation node with the next rank;
if the time length to be checked is longer than or equal to the rated time length, indicating that the primary wind speed fluctuation node is a non-transient node, and calibrating the primary wind speed fluctuation node as a secondary wind speed fluctuation node;
the optimizing module is used for taking adjacent three secondary wind speed fluctuation nodes as a group, and inputting the group of adjacent three secondary wind speed fluctuation nodes into the optimizing model to obtain the sectional duration;
the classification module is used for acquiring the segment duration under each monitoring period and inputting the segment duration into the classification model to obtain the executable duration and the non-executable duration;
the planning module is used for acquiring the historical wind speed parameters under the non-executable duration, measuring and calculating the average value of the historical wind speed parameters to obtain the average wind speed, and planning a corresponding wind energy capturing model according to the average wind speed.
In the above, when the system is executed, the first acquisition module firstly acquires low wind speed information, the low wind speed information comprises a historical wind speed parameter and a current wind speed parameter, the historical wind speed parameter is acquired by providing a reference basis for the current wind speed parameter, a tracking strategy (wind energy capturing model) is planned, after the historical wind speed parameter is determined, a monitoring period is constructed through the sampling module, the historical wind speed parameter under each sampling node in the monitoring period is acquired, the historical wind speed parameter is summarized into a data set to be evaluated, then an evaluation module is combined, a plurality of primary wind speed fluctuation nodes are determined, the duration between adjacent primary wind speed fluctuation nodes is acquired through the second acquisition module, the rated duration is calibrated into a duration to be checked, the rated duration is compared with the duration to be checked through the comparison module, so that whether the primary wind speed node is an instantaneous node is judged, when the primary wind speed node is a non-instantaneous node, the primary wind speed fluctuation node is calibrated into a secondary wind speed fluctuation node, three adjacent secondary wind speed fluctuation nodes are taken as a group, the historical wind speed parameters under each sampling node are acquired into an optimization module, the segment duration is calibrated, the executable duration and the non-executable duration is calculated through the combination module, and the non-executable duration is calculated through the non-executable duration is planned by the comparison module, and the non-executable duration is planned and the non-executable duration is combined with the model.
In a preferred embodiment, the wind energy capturing model includes a first measuring unit, a second measuring unit and a third measuring unit, where the first measuring unit, the second measuring unit and the third measuring unit respectively correspond to an average wind speed lower limit value.
In the foregoing, the first measuring and calculating unit, the second measuring and calculating unit, and the third measuring and calculating unit are respectively a tip speed ratio method, an optimal torque method, and a hill-climbing searching method, which are all commonly used in the prior art, and detailed descriptions thereof are omitted herein.
And an active power control terminal relying on dynamic planning of low wind speeds, comprising:
at least one processor;
and a memory communicatively coupled to the at least one processor;
wherein the memory stores a computer program executable by the at least one processor to enable the at least one processor to perform the active power control method dependent on low wind speed dynamic planning described above.
Those skilled in the art will appreciate that the control terminal of the present invention may be specially designed and manufactured for the required purposes, or may comprise a well-known device in a general purpose computer. These devices have computer programs or applications stored therein that are selectively activated or reconfigured. Such a computer program may be stored in a device (e.g., a computer) readable medium or any type of medium suitable for storing electronic instructions and respectively coupled to a bus, including, but not limited to, any type of disk (including floppy disks, hard disks, optical disks, CD-ROMs, and magneto-optical disks), ROMs (Read-Only memories), RAMs (Random AccessMemory, random access memories), EPROMs (Erasable Programmable Read-Only memories), EEPROMs (Electrically Erasable Programmable Read-Only memories), flash memories, magnetic cards, or optical cards. That is, a readable medium includes any medium that stores or transmits information in a form readable by a device (e.g., a computer)
It should be noted that, in this document, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, apparatus, article, or method that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, apparatus, article, or method. Without further limitation, an element defined by the phrase "comprising one … …" does not exclude the presence of other like elements in a process, apparatus, article or method that comprises the element.
The foregoing is merely a preferred embodiment of the present invention and it should be noted that modifications and adaptations to those skilled in the art may be made without departing from the principles of the present invention, which are intended to be comprehended within the scope of the present invention. Structures, devices and methods of operation not specifically described and illustrated herein, unless otherwise indicated and limited, are implemented according to conventional means in the art.
Claims (10)
1. An active power control method depending on low wind speed dynamic programming is characterized in that: comprising the following steps:
acquiring low wind speed information, wherein the low wind speed information comprises a historical wind speed parameter and a current wind speed parameter;
constructing a plurality of monitoring time periods, setting a plurality of sampling nodes in the monitoring time periods respectively, and summarizing historical wind speed parameters under the sampling nodes into a plurality of data sets to be evaluated;
respectively inputting the historical wind speed parameters in a plurality of monitoring periods into an evaluation model to obtain a plurality of primary wind speed fluctuation nodes;
acquiring the duration time between adjacent first-level wind speed fluctuation nodes, and calibrating the duration time as the duration time to be checked;
acquiring a rated time length and comparing the rated time length with the time length to be checked;
if the time length to be checked is smaller than the rated time length, indicating that the primary wind speed fluctuation node with the next back is an instantaneous node, and continuously comparing with the primary wind speed fluctuation node with the next primary;
if the time length to be checked is greater than or equal to the rated time length, indicating that the primary wind speed fluctuation node is a non-transient node, and calibrating the primary wind speed fluctuation node as a secondary wind speed fluctuation node;
taking adjacent three secondary wind speed fluctuation nodes as a group, and inputting the group into an optimization model to obtain a segmentation duration;
acquiring the segment duration under each monitoring period, and inputting the segment duration into a classification model to obtain executable duration and non-executable duration;
and acquiring the historical wind speed parameter under the non-executable duration, measuring and calculating the average value of the historical wind speed parameter to obtain the average wind speed, inputting the average wind speed into a planning model, and matching the average wind speed with a corresponding wind energy capturing model.
2. The active power control method depending on low wind speed dynamic planning according to claim 1, wherein: the historical wind speed parameter and the current wind speed parameter comprise constant wind, gust wind and gradual change wind.
3. The active power control method depending on low wind speed dynamic planning according to claim 1, wherein: the step of respectively inputting the historical wind speed parameters in the monitoring periods into an evaluation model to obtain a plurality of first-stage wind speed fluctuation nodes comprises the following steps:
acquiring fluctuation quantity of the historical wind speed parameters under adjacent sampling nodes from the data set to be evaluated, and calibrating the fluctuation quantity as the parameters to be checked;
invoking an evaluation function from the evaluation model;
acquiring rated wind speed fluctuation quantity, and inputting the rated wind speed fluctuation quantity and the parameter to be checked into an evaluation function together to obtain wind speed fluctuation deviation quantity;
acquiring an allowable floating threshold value and comparing the allowable floating threshold value with the fluctuation deviation amount of the wind speed;
if the fluctuation deviation of the wind speed is smaller than the allowable floating threshold, judging that the historical wind speed parameter is not fluctuated, and continuously comparing the historical wind speed parameter of the next level;
if the fluctuation deviation of the wind speed is greater than or equal to the allowable floating threshold, judging the fluctuation of the historical wind speed parameter, and calibrating a sampling node corresponding to the fluctuation of the wind speed parameter as a primary wind speed fluctuation node.
4. The active power control method depending on low wind speed dynamic planning according to claim 1, wherein: the step of inputting a group of adjacent three secondary wind speed fluctuation nodes into an optimization model to obtain the segment duration comprises the following steps:
acquiring adjacent three secondary wind speed fluctuation nodes, and respectively calibrating the adjacent three secondary wind speed fluctuation nodes as a first fluctuation node, a second fluctuation node and a third fluctuation node;
acquiring the time length between the first fluctuation node and the second fluctuation node and the time length between the second fluctuation node and the third fluctuation node, and calibrating the time length as a first time length to be optimized and a second time length to be optimized respectively;
calling an optimization function from the optimization model, and inputting the first time length to be optimized and the second time length to be optimized into the optimization function to obtain segmented nodes;
and determining the segment duration from the time period between the adjacent segment nodes.
5. The active power control method depending on low wind speed dynamic planning according to claim 1, wherein: the step of obtaining the segment duration under each monitoring period and inputting the segment duration into a classification model to obtain the executable duration and the non-executable duration comprises the following steps:
acquiring the segmentation duration under the monitoring period;
calling a classification interval from the classification model, respectively comparing the classification interval with the segmentation duration, and judging whether the segmentation duration belongs to the classification interval or not;
if yes, judging the segmentation duration to be non-executable duration;
if not, judging the segmentation duration as executable duration.
6. The active power control method depending on low wind speed dynamic planning according to claim 1, wherein: the steps after the executable time length and the non-executable time length are obtained comprise:
acquiring the number of all non-executable time periods, and calibrating the number as a parameter to be compared;
calculating the occupation ratio of the parameters to be compared in the total executable time length and the total non-executable time length;
acquiring an evaluation threshold value and comparing the evaluation threshold value with the occupation ratio of the parameters to be compared;
if the occupation ratio of the parameters to be compared is larger than or equal to an evaluation threshold, inputting the non-executable time length in the monitoring period into a bias evaluation model to obtain a variation trend of the non-executable time length;
and if the occupation ratio of the parameters to be compared is smaller than an evaluation threshold value, indicating that the non-executable duration in the monitoring period is an instantaneous phenomenon, and not inputting the non-executable duration into a bias evaluation model.
7. The active power control method depending on low wind speed dynamic planning according to claim 1, wherein: inputting the non-executable time length in the monitoring period into a bias evaluation model to obtain the variation trend of the non-executable time length, wherein the step comprises the following steps:
acquiring the number of non-executable time durations in each monitoring period, and calibrating the number as a parameter to be evaluated;
invoking an evaluation function from the bias evaluation model;
inputting the parameters to be evaluated into an evaluation function, and calibrating the output result as a centralized deviation;
if the value of the centralized deviation is larger than zero, the non-executable duration is gradually reduced;
if the value of the centralized deviation is equal to zero, the non-executable duration is indicated to be evenly distributed in a plurality of monitoring periods;
and if the value of the centralized deviation is smaller than zero, the non-executable duration is gradually increased.
8. An active power control system depending on low wind speed dynamic programming, applied to the active power control method depending on low wind speed dynamic programming as claimed in any one of claims 1 to 7, characterized in that: comprising the following steps:
the system comprises a first acquisition module, a second acquisition module and a control module, wherein the first acquisition module is used for acquiring low wind speed information, and the low wind speed information comprises a historical wind speed parameter and a current wind speed parameter;
the sampling module is used for constructing a plurality of monitoring time periods, setting a plurality of sampling nodes in the monitoring time periods respectively, and summarizing the historical wind speed parameters under the sampling nodes into a plurality of data sets to be evaluated;
the evaluation module is used for respectively inputting the historical wind speed parameters in a plurality of monitoring periods into an evaluation model to obtain a plurality of primary wind speed fluctuation nodes;
the second acquisition module is used for acquiring the duration between the adjacent first-level wind speed fluctuation nodes and calibrating the duration as the duration to be checked;
the comparison module is used for obtaining rated time length and comparing the rated time length with the time length to be checked;
if the time length to be checked is smaller than the rated time length, indicating that the primary wind speed fluctuation node with the next back is an instantaneous node, and continuously comparing with the primary wind speed fluctuation node with the next primary;
if the time length to be checked is greater than or equal to the rated time length, indicating that the primary wind speed fluctuation node is a non-transient node, and calibrating the primary wind speed fluctuation node as a secondary wind speed fluctuation node;
the optimizing module is used for taking adjacent three secondary wind speed fluctuation nodes as a group, and inputting the group of adjacent three secondary wind speed fluctuation nodes into an optimizing model to obtain the segmentation duration;
the classification module is used for acquiring the segment duration under each monitoring period and inputting the segment duration into the classification model to obtain the executable duration and the non-executable duration;
and the planning module is used for acquiring the historical wind speed parameters under the non-executable duration, measuring and calculating the average value of the historical wind speed parameters to obtain the average wind speed, and planning a corresponding wind energy capturing model according to the average wind speed.
9. An active power control system in accordance with claim 8, wherein said active power control system is configured to rely on dynamic planning of low wind speeds: the wind energy capturing model comprises a first measuring and calculating unit, a second measuring and calculating unit and a third measuring and calculating unit, wherein the first measuring and calculating unit, the second measuring and calculating unit and the third measuring and calculating unit respectively correspond to an average wind speed lower limit value.
10. An active power control terminal relying on low wind speed dynamic programming, characterized in that: comprising the following steps:
at least one processor;
and a memory communicatively coupled to the at least one processor;
wherein the memory stores a computer program executable by the at least one processor to enable the at least one processor to perform the active power control method dependent on low wind speed dynamic programming of any one of claims 1 to 7.
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