CN112199847B - Water power output fluctuation frequency identification method and system based on time domain and magnitude - Google Patents

Water power output fluctuation frequency identification method and system based on time domain and magnitude Download PDF

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CN112199847B
CN112199847B CN202011102709.4A CN202011102709A CN112199847B CN 112199847 B CN112199847 B CN 112199847B CN 202011102709 A CN202011102709 A CN 202011102709A CN 112199847 B CN112199847 B CN 112199847B
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黄会宝
高志良
罗正英
王勇飞
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Abstract

A method and a system for identifying the fluctuation frequency of the water power output based on time domain and magnitude comprise the steps of analyzing the time sequence of the water power output into fold lines formed by connecting a plurality of points, firstly dividing the time sequence of the whole water power output into a plurality of sub-time sequences according to the time domain length, calculating the discrete values and the change rates of all the sub-time sequences, determining the magnitude dimension, the shape and the integral fluctuation of all the sub-time sequences, then determining the magnitude range of the magnitude dimension, the shape and the integral fluctuation, further counting the occurrence frequency of the magnitude dimension, the shape and the integral fluctuation in the corresponding magnitude range, and obtaining the fluctuation frequency analysis result of the water power output, wherein the larger the frequency is, the more severe the fluctuation of the water power output in the corresponding magnitude range is indicated. The method provides a new technical scheme for quantitatively analyzing the water power fluctuation situation, is beneficial to analyzing the water power fluctuation degree, further provides decision support for reservoir dispatching, and has important significance for water resource development and utilization.

Description

Water power output fluctuation frequency identification method and system based on time domain and magnitude
Technical Field
The invention belongs to the field of water resource analysis, and particularly relates to a method and a system for identifying the fluctuation frequency of hydroelectric power output based on time domain and magnitude.
Background
The study of the fluctuation frequency of the water power output is mainly used for analyzing the running stable situation of the hydropower station, and further provides decision support for reservoir dispatching with flood control, power generation, ecology and the like as targets. The accurate water power fluctuation situation identification has great significance for enhancing flood control safety and scientifically and efficiently utilizing water resources. At present, the fluctuation situation analysis of the water power output at home and abroad mainly focuses on the fluctuation analysis of the whole water power output process, and the conclusion is that the method is based on the analysis of the fluctuation of the whole water power output process and cannot be suitable for the requirement of paying attention to the fluctuation of a specific magnitude in a specific time domain in production practice. In this regard, the ability to identify the frequency of water power fluctuations based on time domain and magnitude provides a more powerful reference for reservoir scheduling.
Disclosure of Invention
The invention aims to overcome the defects of the prior art and provides a hydropower output fluctuation frequency identification method and system based on time domain and magnitude so as to more accurately analyze the fluctuation situation of hydropower output.
In order to achieve the above purpose, the invention provides a method for identifying the fluctuation frequency of the water power output based on time domain and magnitude, which comprises the following steps:
step 1, analyzing a water power output time sequence, wherein the water power output time sequence is obtained by taking time x as an abscissa and water power output q as an ordinate according to the water power output process in a rectangular coordinate system; the analysis process comprises analyzing the water power output time sequence into broken lines composed of several points, setting T points as control points, and sequentially numbering from left to right as 1,2, …, T, and marking the coordinates of the T control points as (x) t ,q t ) T=1, 2, T, the water power time series meter is { x } t ,q t }；
Step 2, setting the length of a segmentation window to be s, wherein s is more than or equal to 3 and less than or equal to T/2;
step 3, dividing the water electric power time sequence into a plurality of sub-time sequences, realizing the following,
water power time series { x } t ,q t Dividing the sub-time sequence into sub-time sequences according to the sliding translation mode, wherein the length of a dividing window is s, and the divided sub-time sequence is { xx } tt ,qq tt } j Where tt=1, 2, …, s, J is the number of the sub-time series, j=1, 2, …, J, j=t-s+1, the J-th sub-time series being
Figure BDA0002725924920000021
Wherein->
Figure BDA0002725924920000022
Figure BDA0002725924920000023
Step 4, calculating each sub-time sequence { xx } tt ,qq tt } j Is calculated as the dispersion value of the measurement dimension fluctuation alpha j The realization is as follows,
Figure BDA0002725924920000024
step 5, calculating each sub-time sequence { xx } tt ,qq tt } j The degree of change of the rate of change, expressed as form dimensional fluctuation beta j The realization is as follows,
first, the rate of change of the sub-time series is calculated
Figure BDA0002725924920000025
When tt=1, 2, …, s-1,/i>
Figure BDA0002725924920000026
Figure BDA0002725924920000027
Is the rate of change between the ttth control point to the tth +1 control point of the sub-time sequence, when tt = s +.>
Figure BDA0002725924920000028
Then, the degree of change of the rate of change at the ttth control point is calculated
Figure BDA0002725924920000029
When tt=1, the element is->
Figure BDA00027259249200000210
When t=2, …, T, +.>
Figure BDA00027259249200000211
Finally, the degree of change of the sub-time sequence change rate is
Figure BDA00027259249200000212
Step 6, calculating each sub-time sequence { xx } tt ,qq tt } j Is denoted as F j ，F j ＝α j ×β j ；
Step 7, setting the magnitude range of the magnitude fluctuation, which is [ alpha ] 0 ,α 1 ],(α 1 ,α 2 ],…,(α a-1 ,α a ],…,(α A-1 ,α A ]A is the magnitude of the magnitude fluctuation, min (α j ) Is alpha obtained in the step 4 j Is the minimum of max (alpha j ) Is alpha obtained in the step 4 j Is the maximum value of (2);
step 8, setting the magnitude range of the shape dimension fluctuation to [ beta ] 0 ,β 1 ],(β 1 ,β 2 ],…,(β b-1 ,β b ],…,(β B-1 ,β B ]B is the magnitude of the shape dimensional fluctuation, min (beta j ) Is beta obtained in step 5 j Is the minimum of max (beta j ) Is beta obtained in step 5 j Is the maximum value of (2);
step 9, setting the magnitude range of the integral fluctuation to be [ F ] 0 ,F 1 ],(F 1 ,F 2 ],…,(F c-1 ,F c ],…,(F C-1 ,F C ]C is the magnitude of the integral fluctuation, min (F j ) Is F obtained in step 6 j Max (F) j ) Is F obtained in step 6 j Is the maximum value of (2);
step 10, counting the frequency of occurrence of the magnitude dimension fluctuation value of all the sub-time sequences in the a-th magnitude range, and counting the frequency as
Figure BDA0002725924920000031
Frequency->
Figure BDA0002725924920000032
The greater the value of (2), the more frequently the hydropower output fluctuates in magnitude in the s-domain;
step 11, counting the frequency of appearance of the shape dimension fluctuation value of all the sub-time sequences in the b-th order range, and counting the frequency as
Figure BDA0002725924920000033
Frequency->
Figure BDA0002725924920000034
The greater the value of (2), the more frequent the shape-dimensional fluctuation of the hydropower forces of that magnitude in the s-time domain;
step 12, counting the frequency of occurrence of the integral fluctuation value of all the sub-time sequences in the range of the c-th order, and counting the frequency as
Figure BDA0002725924920000035
Frequency->
Figure BDA0002725924920000036
The greater the value of (c), the more frequent the overall fluctuation of the hydropower force is of this magnitude in the s-time domain.
The invention also correspondingly provides a water power output fluctuation frequency identification system based on the time domain and the magnitude, which comprises the following modules,
the initial analysis module is used for analyzing a water power output time sequence, wherein the water power output time sequence is obtained by taking time x as an abscissa and water power output q as an ordinate according to the water power output process in a rectangular coordinate system; the analysis process comprises analyzing the water power output time sequence into broken lines composed of several points, setting T points as control points, and sequentially numbering from left to right as 1,2, …, T, and marking the coordinates of the T control points as (x) t ,q t ) T=1, 2, T, the water power time series meter is { x } t ,q t }；
The sub time sequence extraction module is used for dividing the water electric power output time sequence into a plurality of sub time sequences, and is realized as follows,
firstly, setting the length of a segmentation window to be s, wherein s is more than or equal to 3 and less than or equal to T/2;
then the water power is time-series { x } t ,q t Dividing the sub-time sequence into sub-time sequences according to the sliding translation mode, wherein the length of a dividing window is s, and the divided sub-time sequence is { xx } tt ,qq tt } j Where tt=1, 2, …, s, J is the number of the sub-time series, j=1, 2, …, J,j=t-s+1, the J-th sub-time series is
Figure BDA0002725924920000037
Wherein->
Figure BDA0002725924920000038
Figure BDA0002725924920000041
A discrete value extraction module for calculating each sub-time sequence { xx }, respectively tt ,qq tt } j Is calculated as the dispersion value of the measurement dimension fluctuation alpha j The realization is as follows,
Figure BDA0002725924920000042
a change degree extraction module for calculating each sub-time sequence { xx }, respectively tt ,qq tt } j The degree of change of the rate of change, expressed as form dimensional fluctuation beta j The realization is as follows,
first, the rate of change of the sub-time series is calculated
Figure BDA0002725924920000043
When tt=1, 2, …, s-1,/i>
Figure BDA0002725924920000044
Figure BDA0002725924920000045
Is the rate of change between the ttth control point to the tth +1 control point of the sub-time sequence, when tt = s +.>
Figure BDA0002725924920000046
Then, the degree of change of the rate of change at the ttth control point is calculated
Figure BDA0002725924920000047
When tt=1, the element is->
Figure BDA0002725924920000048
When t=2, …, T, +.>
Figure BDA0002725924920000049
Finally, the degree of change of the sub-time sequence change rate is
Figure BDA00027259249200000410
The overall fluctuation extraction module is used for calculating each sub-time sequence { xx } tt ,qq tt } j Is denoted as F j ，F j ＝α j ×β j ；
The magnitude range setting module is used for setting magnitude ranges of quantitative dimension fluctuation, shape dimension fluctuation and integral fluctuation, and is realized as follows,
the magnitude range of the quantitative dimension fluctuation is set to [ alpha ] 0 ,α 1 ],(α 1 ,α 2 ],…,(α a-1 ,α a ],…,(α A-1 ,α A ]A is the magnitude of the magnitude fluctuation, min (α j ) Extracting the alpha obtained in the module for discrete values j Is the minimum of max (alpha j ) Extracting the alpha obtained in the module for discrete values j Is the maximum value of (2);
the magnitude range of the dimension fluctuation is designed to be [ beta ] 0 ,β 1 ],(β 1 ,β 2 ],…,(β b-1 ,β b ],…,(β B-1 ,β B ]B is the magnitude of the shape dimensional fluctuation, min (beta j ) Extracting the obtained beta in the module for changing the degree j Is the minimum of max (beta j ) Extracting the obtained beta in the module for changing the degree j Is the maximum value of (2);
setting the magnitude range of the integral fluctuation to be [ F 0 ,F 1 ],(F 1 ,F 2 ],…,(F c-1 ,F c ],…,(F C-1 ,F C ]C is the magnitude of the integral fluctuation, C is the magnitude of the integral fluctuation, min(F j ) Extracting F obtained in the module for integral fluctuation j Max (F) j ) Extracting F obtained in the module for integral fluctuation j Is the maximum value of (2);
the frequency statistics module is used for counting the frequency of occurrence of the volume dimension, the shape dimension and the integral fluctuation in the corresponding magnitude range, and is realized as follows,
counting the number of times that the magnitude fluctuation value of all the sub-time sequences appears in the a-th magnitude range, and counting the frequency as
Figure BDA0002725924920000051
Frequency->
Figure BDA0002725924920000052
The greater the value of (2), the more frequently the hydropower output fluctuates in magnitude in the s-domain;
counting the frequency of occurrence of the waveform dimension fluctuation value of all the sub-time sequences in the b-th order range, and counting the frequency as
Figure BDA0002725924920000053
Frequency->
Figure BDA0002725924920000054
The greater the value of (2), the more frequent the shape-dimensional fluctuation of the hydropower forces of that magnitude in the s-time domain;
counting the frequency of occurrence of the integral fluctuation value of all the sub-time sequences in the c-th order range, and counting the frequency as
Figure BDA0002725924920000055
Frequency->
Figure BDA0002725924920000056
The greater the value of (c), the more frequent the overall fluctuation of the hydropower force is of this magnitude in the s-time domain.
According to the technical scheme for identifying the fluctuation frequency of the hydroelectric power output based on the time domain and the magnitude, provided by the invention, the fluctuation frequency of the hydroelectric power output under the given time domain and magnitude is automatically extracted to judge the fluctuation situation of the hydroelectric power output, so that a novel judgment method is provided, the result is simple and clear, and the implementation is simple and easy. Compared with the prior art, the method for identifying the fluctuation frequency by taking the time domain and the fluctuation magnitude as the discrimination basis is an important innovation in the technical field, is favorable for judging the fluctuation situation of the water power output, has important significance for the development and the utilization of water resources, and has important popularization and use values.
Drawings
FIG. 1 is a schematic diagram of a water power output time sequence according to an embodiment of the invention.
Fig. 2 is a schematic diagram of fluctuation frequency of the magnitude fluctuation in different magnitude ranges when the time domain is taken 10 according to the embodiment of the invention.
Fig. 3 is a schematic diagram of the fluctuation frequency of the waveform dimension fluctuation in different magnitude ranges when the time domain is taken 10 according to the embodiment of the invention.
Fig. 4 is a schematic diagram of the frequency of the overall fluctuation in the different magnitude range when the time domain is taken 10 according to the embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention more clear, the technical solutions of the present invention will be described below with reference to the embodiments of the present invention and the accompanying drawings.
The embodiment of the invention comprises the following steps that the automatic operation can be realized by adopting a computer software technology when the embodiment is implemented:
step 1, analyzing a water power output time sequence, wherein the water power output time sequence is obtained by taking time x as an abscissa and water power output q as an ordinate in a rectangular coordinate system according to the water power output process (shown in figure 1); the analysis process comprises analyzing the water power output time sequence into broken lines composed of several points, setting T points as control points, and sequentially numbering from left to right as 1,2, …, T, and marking the coordinates of the T control points as (x) t ,q t ) T=1, 2, T, the water power time series meter is { x } t ,q t }；
Step 2, setting the length of a segmentation window to be s, wherein s is more than or equal to 3 and less than or equal to T/2;
step 3, dividing the water electric power time sequence into a plurality of sub-time sequences, realizing the following,
water power time series { x } t ,q t Dividing the sub-time sequence into sub-time sequences according to the sliding translation mode, wherein the length of a dividing window is s, and the divided sub-time sequence is { xx } tt ,qq tt } j Where tt=1, 2, …, s, J is the number of the sub-time series, j=1, 2, …, J, j=t-s+1, the J-th sub-time series being
Figure BDA0002725924920000061
Wherein->
Figure BDA0002725924920000062
Figure BDA0002725924920000063
Step 4, calculating each sub-time sequence { xx } tt ,qq tt } j Is calculated as the dispersion value of the measurement dimension fluctuation alpha j The realization is as follows,
Figure BDA0002725924920000064
step 5, calculating each sub-time sequence { xx } tt ,qq tt } j The degree of change of the rate of change, expressed as form dimensional fluctuation beta j The realization is as follows,
first, the rate of change of the sub-time series is calculated
Figure BDA0002725924920000065
When tt=1, 2, …, s-1,/i>
Figure BDA0002725924920000066
Is the rate of change between the ttth control point to the tth +1 control point of the sub-time sequence, when tt = s +.>
Figure BDA0002725924920000068
Then, the degree of change of the rate of change at the ttth control point is calculated
Figure BDA0002725924920000069
When tt=1, the element is->
Figure BDA00027259249200000610
When t=2, …, T, +.>
Figure BDA0002725924920000071
Finally, the degree of change of the sub-time sequence change rate is
Figure BDA0002725924920000072
Step 6, calculating each sub-time sequence { xx } tt ,qq tt } j Is denoted as F j ，F j ＝α j ×β j ；
Step 7, setting the magnitude range of the magnitude fluctuation, which is [ alpha ] 0 ,α 1 ],(α 1 ,α 2 ],…,(α a-1 ,α a ],…,(α A-1 ,α A ]A is the magnitude of the magnitude fluctuation, min (α j ) Is alpha obtained in the step 4 j Is the minimum of max (alpha j ) Is alpha obtained in the step 4 j Is the maximum value of (2);
step 8, setting the magnitude range of the shape dimension fluctuation to [ beta ] 0 ,β 1 ],(β 1 ,β 2 ],…,(β b-1 ,β b ],…,(β B-1 ,β B ]B is the magnitude of the shape dimensional fluctuation, min (beta j ) Is beta obtained in step 5 j Is the minimum of max (beta j ) Is beta obtained in step 5 j Is the maximum value of (2);
step 9, setting the magnitude range of the integral fluctuation to be [ F ] 0 ,F 1 ],(F 1 ,F 2 ],…,(F c-1 ,F c ],…,(F C-1 ,F C ]C is the magnitude of the integral fluctuation, min (F j ) Is F obtained in step 6 j Max (F) j ) Is F obtained in step 6 j Is the maximum value of (2);
step 10, counting the frequency of occurrence of the magnitude dimension fluctuation value of all the sub-time sequences in the a-th magnitude range, and counting the frequency as
Figure BDA0002725924920000073
Frequency->
Figure BDA0002725924920000074
The greater the value of (a) is, the more frequent the magnitude of the water power fluctuates in the magnitude of s time domain, as shown in FIG. 2, the fluctuation frequency of the magnitude fluctuation in the different magnitude ranges when the time domain takes 10 is shown, and the result shows that the magnitude fluctuation of the water power takes 10 (1113,1241]Sum (1241,1389)]Relatively frequent fluctuations of large magnitude;
step 11, counting the frequency of appearance of the shape dimension fluctuation value of all the sub-time sequences in the b-th order range, and counting the frequency as
Figure BDA0002725924920000075
Frequency->
Figure BDA0002725924920000076
The greater the value of (a) the more frequently the shape-dimensional fluctuation of the magnitude of the water power in the s time domain, as shown in fig. 3, the fluctuation frequency of the shape-dimensional fluctuation in the range of different magnitude levels when the time domain takes 10, and the result shows that the shape-dimensional fluctuation of the water power takes 10 (0.78,0.94]Sum (1.1,1.25)]The fluctuation in the middle of the magnitude is relatively frequent;
step 12, counting the frequency of occurrence of the shape dimension fluctuation value of all the sub-time sequences in the c-th order range, and counting the frequency as
Figure BDA0002725924920000077
Frequency->
Figure BDA0002725924920000078
The larger the value of (a) is, the more frequent the overall fluctuation of the water power in the magnitude of s time domain is, as shown in the fluctuation frequency of the shape dimension fluctuation in the range of different magnitude levels when the time domain is taken to 10 in the figure 4, the result shows that the overall fluctuation of the water power is taken to 10 in the time domain [44,287 ]]Fluctuations of smaller magnitude are relatively frequent.
When the invention is embodied, the invention can also be realized by adopting a water power fluctuation frequency identification system based on time domain and magnitude, and the invention comprises the following modules:
the initial analysis module is used for analyzing a water power output time sequence, wherein the water power output time sequence is obtained by taking time x as an abscissa and water power output q as an ordinate according to the water power output process in a rectangular coordinate system; the analysis process comprises analyzing the water power output time sequence into broken lines composed of several points, setting T points as control points, and sequentially numbering from left to right as 1,2, …, T, and marking the coordinates of the T control points as (x) t ,q t ) T=1, 2, T, the water power time series meter is { x } t ,q t }；
The sub time sequence extraction module is used for dividing the water electric power output time sequence into a plurality of sub time sequences, and is realized as follows,
firstly, setting the length of a segmentation window to be s, wherein s is more than or equal to 3 and less than or equal to T/2;
then the water power is time-series { x } t ,q t Dividing the sub-time sequence into sub-time sequences according to the sliding translation mode, wherein the length of a dividing window is s, and the divided sub-time sequence is { xx } tt ,qq tt } j Where tt=1, 2, …, s, J is the number of the sub-time series, j=1, 2, …, J, j=t-s+1, the J-th sub-time series being
Figure BDA0002725924920000081
Wherein->
Figure BDA0002725924920000082
Figure BDA0002725924920000083
A discrete value extraction module for calculating each sub-time sequence { xx }, respectively tt ,qq tt } j Is calculated as the dispersion value of the measurement dimension fluctuation alpha j The realization is as follows,
Figure BDA0002725924920000084
a change degree extraction module for calculating each sub-time sequence { xx }, respectively tt ,qq tt } j The degree of change of the rate of change, expressed as form dimensional fluctuation beta j The realization is as follows,
first, the rate of change of the sub-time series is calculated
Figure BDA0002725924920000085
When tt=1, 2, …, s-1,/i>
Figure BDA0002725924920000086
Figure BDA0002725924920000087
Is the rate of change between the ttth control point to the tth +1 control point of the sub-time sequence, when tt = s +.>
Figure BDA0002725924920000088
Then, the degree of change of the rate of change at the ttth control point is calculated
Figure BDA0002725924920000091
When tt=1, the element is->
Figure BDA0002725924920000092
When t=2, …, T, +.>
Figure BDA0002725924920000093
Finally, the degree of change of the sub-time sequence change rate is
Figure BDA0002725924920000094
The overall fluctuation extraction module is used for calculating each sub-time sequence { xx } tt ,qq tt } j Is denoted as F j ，F j ＝α j ×β j ；
The magnitude range setting module is used for setting magnitude ranges of quantitative dimension fluctuation, shape dimension fluctuation and integral fluctuation, and is realized as follows,
the magnitude range of the quantitative dimension fluctuation is set to [ alpha ] 0 ,α 1 ],(α 1 ,α 2 ],…,(α a-1 ,α a ],…,(α A-1 ,α A ]A is the magnitude of the magnitude fluctuation, min (α j ) Extracting the alpha obtained in the module for discrete values j Is the minimum of max (alpha j ) Extracting the alpha obtained in the module for discrete values j Is the maximum value of (2);
the magnitude range of the dimension fluctuation is designed to be [ beta ] 0 ,β 1 ],(β 1 ,β 2 ],…,(β b-1 ,β b ],…,(β B-1 ,β B ]B is the magnitude of the shape dimensional fluctuation, min (beta j ) Extracting the obtained beta in the module for changing the degree j Is the minimum of max (beta j ) Extracting the obtained beta in the module for changing the degree j Is the maximum value of (2);
setting the magnitude range of the integral fluctuation to be [ F 0 ,F 1 ],(F 1 ,F 2 ],…,(F c-1 ,F c ],…,(F C-1 ,F C ]C is the magnitude of the integral fluctuation, min (F j ) Extracting F obtained in the module for integral fluctuation j Max (F) j ) Extracting F obtained in the module for integral fluctuation j Is the maximum value of (2);
the frequency statistics module is used for counting the frequency of occurrence of the volume dimension, the shape dimension and the integral fluctuation in the corresponding magnitude range, and is realized as follows,
counting the number of times that the magnitude fluctuation value of all the sub-time sequences appears in the a-th magnitude range, and counting the frequency as
Figure BDA0002725924920000095
Frequency->
Figure BDA0002725924920000096
The greater the value of (2), the more frequently the hydropower output fluctuates in magnitude in the s-domain;
counting the frequency of occurrence of the waveform dimension fluctuation value of all the sub-time sequences in the b-th order range, and counting the frequency as
Figure BDA0002725924920000097
Frequency->
Figure BDA0002725924920000098
The greater the value of (2), the more frequent the shape-dimensional fluctuation of the hydropower forces of that magnitude in the s-time domain;
counting the frequency of occurrence of the integral fluctuation value of all the sub-time sequences in the c-th order range, and counting the frequency as
Figure BDA0002725924920000101
Frequency->
Figure BDA0002725924920000102
The greater the value of (c), the more frequent the overall fluctuation of the hydropower force is of this magnitude in the s-time domain.
The method is mainly applied to the judgment of the fluctuation situation of the water power output, and in the application of water resource analysis, the water power output process, the time domain length and the fluctuation magnitude are taken as inputs, so that the corresponding fluctuation frequency of the water power output under the given time domain length and fluctuation magnitude can be automatically identified, and the aim of identifying the fluctuation situation of the water power output more accurately is achieved. Compared with the prior related art, the method has the innovation that the fluctuation frequency of the hydroelectric power is identified through the time domain and the fluctuation magnitude. In view of this, the rationality of the technical scheme of the invention can be verified by applying the invention to the numerical value of fluctuation frequency obtained after the water power fluctuation situation analysis. As can be seen from fig. 2 to 4, the technical solution provided by the present invention can indeed identify the frequency of fluctuation of the hydro-electric power in a given time domain and fluctuation magnitude.
According to the embodiment results, the technical scheme provided by the invention identifies the fluctuation frequency of the water power output and illustrates the effectiveness of the invention. The invention can automatically and effectively identify the fluctuation frequency under a given time domain and fluctuation magnitude, and provides decision support for water resource development and utilization.
It should be emphasized that the examples described herein are illustrative rather than limiting, and therefore the invention is not limited to the examples described in the detailed description, but rather falls within the scope of the invention as defined by other embodiments derived from the technical solutions of the invention by those skilled in the art.
What is not described in detail in this specification is prior art known to those skilled in the art.

Claims (2)

1. The method for identifying the fluctuation frequency of the hydroelectric power output based on the time domain and the magnitude is characterized by comprising the following steps:
step 1, analyzing a water power output time sequence, wherein the water power output time sequence is obtained by taking time x as an abscissa and water power output q as an ordinate according to the water power output process in a rectangular coordinate system; the analysis process comprises analyzing the water power output time sequence into broken lines composed of several points, setting T points as control points, and sequentially numbering from left to right as 1,2, …, T, and marking the coordinates of the T control points as (x) t ,q t ) T=1, 2, T, the water power time series meter is { x } t ,q t }；
Step 2, setting the length of a segmentation window to be s, wherein s is more than or equal to 3 and less than or equal to T/2;
step 3, dividing the water electric power time sequence into a plurality of sub-time sequences, realizing the following,
water power time series { x } t ,q t Dividing the sub-time sequence into sub-time sequences according to the sliding translation mode, wherein the length of a dividing window is s, and the divided sub-time sequence is { xx } tt ,qq tt } j Where tt=1, 2, …, s, j is the number of the sub-time seriesJ=1, 2, …, J, j=t-s+1, the J-th sub-time series being
Figure FDA0004177897070000011
Wherein the method comprises the steps of
Figure FDA0004177897070000012
Figure FDA0004177897070000013
Step 4, calculating each sub-time sequence { xx } tt ,qq tt } j Is calculated as the dispersion value of the measurement dimension fluctuation alpha j The realization is as follows,
Figure FDA0004177897070000014
step 5, calculating each sub-time sequence { xx } tt ,qq tt } j The degree of change of the rate of change, expressed as form dimensional fluctuation beta j The realization is as follows,
first, the rate of change of the sub-time series is calculated
Figure FDA0004177897070000015
When tt=1, 2, …, s-1,/i>
Figure FDA0004177897070000016
Figure FDA0004177897070000017
Is the rate of change between the ttth control point to the tth +1 control point of the sub-time sequence, when tt = s +.>
Figure FDA0004177897070000018
Then, the degree of change of the rate of change at the ttth control point is calculated
Figure FDA0004177897070000019
When tt=1, the element is->
Figure FDA00041778970700000110
When t=2, …, T, +.>
Figure FDA00041778970700000111
Finally, the degree of change of the sub-time sequence change rate is
Figure FDA00041778970700000112
Step 6, calculating each sub-time sequence { xx } tt ,qq tt } j Is denoted as F j ，F j ＝α j ×β j ；
Step 7, setting the magnitude range of the magnitude fluctuation, which is [ alpha ] 0 ,α 1 ],(α 1 ,α 2 ],…,(α a-1 ,α a ],…,(α A -1 ,α A ]A is the magnitude of the magnitude fluctuation, min (α j ) Is alpha obtained in the step 4 j Is the minimum of max (alpha j ) Is alpha obtained in the step 4 j Is the maximum value of (2);
step 8, setting the magnitude range of the shape dimension fluctuation to [ beta ] 0 ,β 1 ],(β 1 ,β 2 ],…,(β b-1 ,β b ],…,(β B-1 ,β B ]B is the magnitude of the shape dimensional fluctuation, min (beta j ) Is beta obtained in step 5 j Is the minimum of max (beta j ) Is beta obtained in step 5 j Is the maximum value of (2);
step 9, setting the magnitude range of the integral fluctuation to be [ F ] 0 ,F 1 ],(F 1 ,F 2 ],…,(F c-1 ,F c ],…,(F C-1 ,F C ]C is the magnitude of the integral fluctuation, min (F j ) Is F obtained in step 6 j Is set to be a minimum value of (c),max(F j ) Is F obtained in step 6 j Is the maximum value of (2);
step 10, counting the frequency of the occurrence of the magnitude dimension fluctuation value of all the sub-time sequences in the a-th magnitude range, and counting the frequency as
Figure FDA0004177897070000021
Frequency->
Figure FDA0004177897070000026
The greater the value of (2), the more frequently the hydropower output fluctuates in magnitude in the s-domain;
step 11, counting the frequency of appearance of the shape dimension fluctuation value of all the sub-time sequences in the b-th order range, and counting the frequency as
Figure FDA0004177897070000022
Frequency->
Figure FDA0004177897070000023
The greater the value of (2), the more frequent the shape-dimensional fluctuation of the hydropower forces of that magnitude in the s-time domain;
step 12, counting the frequency of the occurrence of the integral fluctuation value of all the sub-time sequences in the range of the c-th order, and counting the frequency as
Figure FDA0004177897070000024
Frequency->
Figure FDA0004177897070000025
The greater the value of (2), the more frequently the overall fluctuation of the hydropower output is of this magnitude in the s-domain;
the water power output process, the time domain length and the fluctuation magnitude are used as inputs, so that the corresponding fluctuation frequency of the water power output under the given time domain length and fluctuation magnitude can be automatically identified.
2. A water power output fluctuation frequency identification system based on time domain and magnitude is characterized in that: comprising a module which comprises a plurality of modules,
the initial analysis module is used for analyzing a water power output time sequence, wherein the water power output time sequence is obtained by taking time x as an abscissa and water power output q as an ordinate according to the water power output process in a rectangular coordinate system; the analysis process comprises analyzing the water power output time sequence into broken lines composed of several points, setting T points as control points, and sequentially numbering from left to right as 1,2, …, T, and marking the coordinates of the T control points as (x) t ,q t ) T=1, 2, T, the water power time series meter is { x } t ,q t }；
The sub time sequence extraction module is used for dividing the water electric power output time sequence into a plurality of sub time sequences, and is realized as follows,
firstly, setting the length of a segmentation window to be s, wherein s is more than or equal to 3 and less than or equal to T/2;
then the water power is time-series { x } t ,q t Dividing the sub-time sequence into sub-time sequences according to the sliding translation mode, wherein the length of a dividing window is s, and the divided sub-time sequence is { xx } tt ,qq tt } j Where tt=1, 2, …, s, J is the number of the sub-time series, j=1, 2, …, J, j=t-s+1, the J-th sub-time series being
Figure FDA0004177897070000031
Wherein->
Figure FDA0004177897070000032
Figure FDA0004177897070000033
A discrete value extraction module for calculating each sub-time sequence { xx }, respectively tt ,qq tt } j Is calculated as the dispersion value of the measurement dimension fluctuation alpha j The realization is as follows,
Figure FDA0004177897070000034
a change degree extraction module for calculating each sub-time sequence { xx }, respectively tt ,qq tt } j The degree of change of the rate of change, expressed as form dimensional fluctuation beta j The realization is as follows,
first, the rate of change of the sub-time series is calculated
Figure FDA0004177897070000035
When tt=1, 2, …, s-1,/i>
Figure FDA0004177897070000036
Figure FDA0004177897070000037
Is the rate of change between the ttth control point to the tth +1 control point of the sub-time sequence, when tt = s +.>
Figure FDA0004177897070000038
Then, the degree of change of the rate of change at the ttth control point is calculated
Figure FDA0004177897070000039
When tt=1, the element is->
Figure FDA00041778970700000310
When t=2, …, T, +.>
Figure FDA00041778970700000311
Finally, the degree of change of the sub-time sequence change rate is
Figure FDA00041778970700000312
The overall fluctuation extraction module is used for calculating each sub-time sequence { xx } tt ,qq tt } j Is denoted as F j ，F j ＝α j ×β j ；
The magnitude range setting module is used for setting magnitude ranges of quantitative dimension fluctuation, shape dimension fluctuation and integral fluctuation, and is realized as follows,
the magnitude range of the quantitative dimension fluctuation is set to [ alpha ] 0 ,α 1 ],(α 1 ,α 2 ],…,(α a- 1,α a ],…,(α A-1 ,α A ]A is the magnitude of the magnitude fluctuation, min (α j ) Extracting the alpha obtained in the module for discrete values j Is the minimum of max (alpha j ) Extracting the alpha obtained in the module for discrete values j Is the maximum value of (2);
the magnitude range of the dimension fluctuation is designed to be [ beta ] 0 ,β 1 ],(β 1 ,β 2 ],…,(β b-1 ,β b ],…,(β B-1 ,β B ]B is the magnitude of the shape dimensional fluctuation, min (beta j ) Extracting the obtained beta in the module for changing the degree j Is the minimum of max (beta j ) Extracting the obtained beta in the module for changing the degree j Is the maximum value of (2);
setting the magnitude range of the integral fluctuation to be [ F 0 ,F 1 ],(F 1 ,F 2 ],…,(F c-1 ,F c ],…,(F C-1 ,F C ]C is the magnitude of the integral fluctuation, min (F j ) Extracting F obtained in the module for integral fluctuation j Max (F) j ) Extracting F obtained in the module for integral fluctuation j Is the maximum value of (2);
the frequency statistics module is used for counting the frequency of occurrence of the volume dimension, the shape dimension and the integral fluctuation in the corresponding magnitude range, and is realized as follows,
counting the frequency of the occurrence of the magnitude fluctuation value of all the sub-time sequences in the a-th magnitude range, and counting the frequency as
Figure FDA0004177897070000041
Frequency->
Figure FDA0004177897070000042
The greater the value of (2), the waterThe more frequent the magnitude of the electric force fluctuates in the s time domain;
counting the occurrence times of the waveform dimension fluctuation values of all the sub-time sequences in the b-th order range, and counting the frequency as
Figure FDA0004177897070000043
Frequency->
Figure FDA0004177897070000044
The greater the value of (2), the more frequent the shape-dimensional fluctuation of the hydropower forces of that magnitude in the s-time domain;
counting the frequency of the occurrence of the integral fluctuation value of all the sub-time sequences in the c-th order range, and counting the frequency as
Figure FDA0004177897070000045
Frequency->
Figure FDA0004177897070000046
The greater the value of (c), the more frequent the overall fluctuation of the hydropower force is of this magnitude in the s-time domain.
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