CN114139834A - Small hydropower frequency prediction method based on weighted distribution - Google Patents

Small hydropower frequency prediction method based on weighted distribution Download PDF

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CN114139834A
CN114139834A CN202111543227.7A CN202111543227A CN114139834A CN 114139834 A CN114139834 A CN 114139834A CN 202111543227 A CN202111543227 A CN 202111543227A CN 114139834 A CN114139834 A CN 114139834A
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interval
frequency
value
prediction
standard
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陈志峰
王智东
冯锺浩
吴�灿
王玕
周培源
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Guangzhou City University of Technology
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Guangzhou City University of Technology
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Priority to PCT/CN2022/132412 priority patent/WO2023109417A1/en
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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/04—Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
    • G06Q10/063—Operations research, analysis or management
    • G06Q10/0639—Performance analysis of employees; Performance analysis of enterprise or organisation operations
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q50/00—Systems or methods specially adapted for specific business sectors, e.g. utilities or tourism
    • G06Q50/06—Electricity, gas or water supply
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y04—INFORMATION OR COMMUNICATION TECHNOLOGIES HAVING AN IMPACT ON OTHER TECHNOLOGY AREAS
    • Y04S—SYSTEMS INTEGRATING TECHNOLOGIES RELATED TO POWER NETWORK OPERATION, COMMUNICATION OR INFORMATION TECHNOLOGIES FOR IMPROVING THE ELECTRICAL POWER GENERATION, TRANSMISSION, DISTRIBUTION, MANAGEMENT OR USAGE, i.e. SMART GRIDS
    • Y04S10/00—Systems supporting electrical power generation, transmission or distribution
    • Y04S10/50—Systems or methods supporting the power network operation or management, involving a certain degree of interaction with the load-side end user applications

Abstract

The invention provides a small hydropower frequency prediction method based on weighted distribution; setting the numerical values of the same prediction interval and standard interval; calculating the frequency of a prediction interval after the current time point at the reference frequency of the standard interval of the small hydropower running record; dividing the running time of the small hydropower station into n standard intervals A; then calculating a corresponding weighted value K of each standard interval A; the average frequency E of the prediction interval is then calculated. Setting the value range of the numerical value n; setting the minimum value of the value n to be 2, wherein the values of the standard interval A and the prediction interval C are the same; calculating the prediction frequency of the small hydropower station operation in the prediction interval C through the reference frequency of the small hydropower station operation in more than two standard intervals A; if n is not an integer, taking the integer from n downwards, and calculating by using a complete standard interval; the accuracy is high; setting the maximum value of the value n to be 10, and improving the reliability of the result; the calculation amount is reduced.

Description

Small hydropower frequency prediction method based on weighted distribution
Technical Field
The invention relates to the technical field of power systems, in particular to a small hydropower frequency prediction method based on weighted distribution.
Background
The problem of power grid frequency fluctuation of small hydropower plants is more prominent, one of the existing small hydropower plant frequency adjusting modes is adjusting according to frequency change, and because a water turbine has inertia, the adjusting mode cannot timely control the frequency within a stable range; the other method is to adjust in advance through a large amount of data base prediction frequency, and the method usually needs a large memory space, while the control equipment of small hydropower generally does not have a sufficient amount of memory space, so that the accuracy of the adjusting method is lost.
Disclosure of Invention
The invention provides a small hydropower station frequency prediction method based on weighted distribution, which accurately predicts the frequency of a small hydropower station through the current running time of the small hydropower station.
In order to achieve the purpose, the technical scheme of the invention is as follows: a small hydropower frequency prediction method based on weighted distribution comprises the following steps;
step (1), taking the last frequency of the last second in each minute of small hydropower station operation as the reference frequency per minute; recording the current numerical value of the reference frequency generated by the small hydropower station; the value of each reference frequency generated every minute is stored separately.
Setting numerical values of a prediction interval C and a standard interval A; the prediction interval C is a time period of the frequency to be predicted within X minutes after the current time point; the standard interval a is a time period close to the time axis of the prediction interval C and having a reference frequency; the prediction interval C, the standard interval A and the X have the same value.
Step (3), removing the standard interval A from the current numerical value of the reference frequency to obtain a numerical value n; the number n is the number of standard intervals A.
Step (4), judging whether the numerical value n meets the condition that n is more than or equal to 10 and more than or equal to 2; if yes, performing the step (4.1); if not, go to step (2) to reconfirm the C, A and X values.
Step (4.1) judging whether the numerical value n is an integer; if yes, performing the step (5); if not, the numerical value n is an integer downwards, and then the step (5) is carried out.
Step (5) passing a formula kn-1=10（1-n）*5（n-1）And formula k1+k2+kn-1+kn= 1; and respectively calculating the corresponding weighted value K of each standard interval A.
Step (6) judging kn-1=knWhether the result is true or not; if yes, performing the step (7); if not, the step (2) is carried out to reconfirm the C, A and the X values; k is a radical ofnThe weighting value is corresponding to the farthest standard interval A from the time axis of the prediction interval C.
And (7) respectively calculating the numerical value and the f of the reference frequency in each standard interval A.
Step (8) by the formula F = k1*f1+k2*f2+kn-1*fn-1+ kn*fnAnd calculating the total frequency F of the prediction interval.
Step (9) by the formula E = F/a; the average frequency E of the prediction interval is calculated.
In the method, the small hydropower station generates a reference frequency per minute; firstly, recording the number of reference frequencies, namely obtaining the running time of the small hydropower station; then determining the numerical value of the prediction interval C, and determining the duration of the prediction interval C; then, a standard interval A is removed through the current numerical value of the reference frequency, and the running time of the small hydropower station is divided into n standard intervals A; then calculating a corresponding weighted value K of each standard interval A; the average frequency E of the prediction interval is then calculated. Setting the value range of the numerical value n; setting the minimum value of the value n to be 2, wherein the values of the standard interval A and the prediction interval C are the same; calculating the prediction frequency of the small hydropower station operation in the prediction interval C through the reference frequency of the small hydropower station operation in more than two standard intervals A; if n is not an integer, taking the integer from n downwards, and calculating by using a complete standard interval; the accuracy is high; at the same time, set a valueThe maximum value of n is 10, so that the reliability of the result is improved; the calculation amount is reduced; then calculating a corresponding weighted value k of each standard interval A; by weighting values of the same kn-1=knThe accuracy of the predicted frequency can be ensured.
Further, step (9) is followed by steps (10) - (12).
Step (10), judging whether E is less than or equal to 49.5 Hz; if so, increasing the power of the small hydropower station; if not, go to step (11).
Step (11), judging whether E is greater than or equal to 50.2 Hz; if so, adjusting the power of the small hydropower station to be small; if not, go to step (12).
And (12) not adjusting the power of the small hydropower station.
Further, the C, A and X values are greater than 1 and less than 30.
Further, the step (1) further comprises: setting a threshold value of the reference frequency; if the current quantity value of the reference frequency is larger than the threshold value of the reference frequency; calculating a difference z between the current magnitude value of the reference frequency and a threshold value of the reference frequency; from the point in time furthest from the prediction interval C, the z reference frequencies are cleared.
The method comprises the steps of clearing z reference frequencies; keeping the current magnitude of the reference frequency equal to the threshold value of the reference frequency; the requirement of the storage capacity of small hydropower equipment is reduced; meanwhile, the excessive quantity value of the reference frequency is avoided; leading to an increase in the amount of computation; simultaneously, calculating from the time point farthest from the prediction interval C; the time period during which the reference frequency is thus generated is close to the prediction interval C; the predicted frequency is more accurate.
Further, after the step (2) is completed, the step (3.1) is carried out; then, steps (4) to (9) are performed.
Step (3.1): removing the standard interval A by using the quantity value of part of the reference frequencies in the currently stored reference frequencies to obtain a quantity value n; the number n is the number of standard intervals A.
Further, according to the time axis, the standard interval closest to the prediction interval C is the first standard interval a1(ii) a Distance betweenThe standard interval farthest from the prediction interval C is the nth standard interval An(ii) a In the step (7), the first standard interval A is selected1The calculation of the value f of the reference frequency in each standard interval is started.
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FIG. 1 is a flow chart of the invention.
Detailed Description
The present invention will be described in further detail with reference to the accompanying drawings and specific embodiments.
As shown in fig. 1; a small hydropower frequency prediction method based on weighted distribution comprises the following steps;
step (1), taking the last frequency of the last second in each minute of small hydropower station operation as the reference frequency per minute; recording the current numerical value of the reference frequency generated by the small hydropower station; the value of each reference frequency generated every minute is stored separately.
Setting numerical values of a prediction interval C and a standard interval A; the prediction interval C is a time period of the frequency to be predicted within X minutes after the current time point; the standard interval a is a time period close to the time axis of the prediction interval C and having a reference frequency; the prediction interval C, the standard interval A and the X have the same value. In this implementation, the values of C, A and X are greater than 1 and less than 30.
Step (3), removing the standard interval A from the current numerical value of the reference frequency to obtain a numerical value n; the number n is the number of standard intervals A.
Step (4), judging whether the numerical value n meets the condition that n is more than or equal to 10 and more than or equal to 2; if yes, performing the step (4.1); if not, go to step (2) to reconfirm the C, A and X values.
Step (4.1) judging whether the numerical value n is an integer; if yes, performing the step (5); if not, the numerical value n is an integer downwards, and then the step (5) is carried out.
Step (5) passing a formula kn-1=10（1-n）*5（n-1）And formula k1+k2+kn-1+kn= 1; and respectively calculating the corresponding weighted value K of each standard interval A.
Step (6) judging kn-1=knWhether the result is true or not; if yes, performing the step (7); if not, the step (2) is carried out to reconfirm the C, A and the X values; k is a radical ofnThe weighting value is corresponding to the farthest standard interval A from the time axis of the prediction interval C.
And (7) respectively calculating the numerical value and the f of the reference frequency in each standard interval A.
Step (8) by the formula F = k1*f1+k2*f2+kn-1*fn-1+ kn*fnAnd calculating the total frequency F of the prediction interval.
Step (9) by the formula E = F/a; the average frequency E of the prediction interval is calculated.
In the method, the small hydropower station generates a reference frequency per minute; firstly, recording the number of reference frequencies, namely obtaining the running time of the small hydropower station; then determining the numerical value of the prediction interval C, and determining the duration of the prediction interval C; then, a standard interval A is removed through the current numerical value of the reference frequency, and the running time of the small hydropower station is divided into n standard intervals A; then calculating a corresponding weighted value K of each standard interval A; the average frequency E of the prediction interval is then calculated. Setting the value range of the numerical value n; setting the minimum value of the value n to be 2, wherein the values of the standard interval A and the prediction interval C are the same; calculating the prediction frequency of the small hydropower station operation in the prediction interval C through the reference frequency of the small hydropower station operation in more than two standard intervals A; if n is not an integer, taking the integer from n downwards, and calculating by using a complete standard interval; the accuracy is high; meanwhile, the maximum value of the value n is set to be 10, so that the reliability of the result is improved; the calculation amount is reduced; then calculating a corresponding weighted value k of each standard interval A; by the same value of kn-1=knThe accuracy of the predicted frequency can be ensured.
The method comprises the following steps:
step (9) is followed by steps (10) - (12).
Step (10), judging whether E is less than or equal to 49.5 Hz; if so, increasing the power of the small hydropower station; if not, go to step (11).
Step (11), judging whether E is greater than or equal to 50.2 Hz; if so, adjusting the power of the small hydropower station to be small; if not, go to step (12).
And (12) not adjusting the power of the small hydropower station.
The step (1) further comprises: setting a threshold value of the reference frequency; if the current quantity value of the reference frequency is larger than the threshold value of the reference frequency; calculating a difference z between the current magnitude value of the reference frequency and a threshold value of the reference frequency; from the point in time furthest from the prediction interval C, the z reference frequencies are cleared. If the threshold value of the reference frequency is 300; the current magnitude value of the reference frequency is 330; calculating the difference value between the current quantity value of the reference frequency and the threshold value of the reference frequency to be 30; the reference frequency recorded at the maximum of 30 minutes from the prediction interval C is cleared.
The method comprises the steps of clearing z reference frequencies; keeping the current magnitude of the reference frequency equal to the threshold value of the reference frequency; the requirement of the storage capacity of small hydropower equipment is reduced; meanwhile, the excessive quantity value of the reference frequency is avoided; leading to an increase in the amount of computation; simultaneously, calculating from the time point farthest from the prediction interval C; the time period during which the reference frequency is thus generated is close to the prediction interval C; the predicted frequency is more accurate.
In the present embodiment, the standard section closest to the prediction section C is the first standard section a in terms of the time axis1(ii) a The standard interval farthest from the prediction interval C is the nth standard interval An(ii) a The frequency of the prediction interval C is calculated from the standard interval close to the prediction interval C, so that the accuracy is high. In the step (5), knThe weighted value of the standard interval farthest from the prediction interval C is obtained; k is a radical ofn-1The weighted value of the standard interval before the standard interval farthest from the prediction interval C. By judging kn-1=knWhether the result is true or not; and comparing the weighted values of the two standard intervals farthest from the prediction interval C, and if the weighted values are the same, ensuring the accuracy of the prediction frequency.
In this implementation, values for 10 reference frequencies were previously stored; the frequency within 5 minutes after the prediction is needed as an example.
Step (2) is carried out, and the numerical values of the prediction interval C and the standard interval A are set to be 5; the frequency within 5 minutes after the current time point is predicted.
Step (3), removing the numerical value of the standard interval A from the numerical value of the current reference frequency 10 to obtain a numerical value 2 of n; the number of standard intervals A obtained was 2.
And (4) carrying out the step (4), wherein the value of n is more than or equal to 10 and more than or equal to 2.
And (4.1) judging that n is an integer.
Proceeding to step (5), calculate k1And k2All values of (a) are 0.5.
Carrying out step (6), Kn-1=KnThis is true.
Proceeding to step (7), calculating the standard interval A1I.e. the sum f of the reference frequency values in minutes 5 to 101(ii) a Calculating a standard interval A2I.e. the sum f of the reference frequency values in minutes 0 to 52. f1 is given a weight value of k1，f2The corresponding weight value is k2。
And (5) carrying out the steps (8) to (12).
Previously stored values for 10 reference frequencies; the frequency within 4 minutes after the prediction is needed is taken as an example.
Step (2) is carried out, and the numerical values of the prediction interval C and the standard interval A are set to be 4; the frequency within 4 minutes after the current time point is predicted.
Step (3) is carried out, the numerical value 10 of the current reference frequency is removed from the numerical value of the standard interval A, and the numerical value of n is obtained to be 2.5; the number of standard intervals A obtained was 2.5.
And (4) carrying out the step (4), wherein the value of n is more than or equal to 10 and more than or equal to 2.
Carrying out the step (4.1), judging that n is not an integer, and taking the integer downwards; namely, taking n as 2; the number of standard intervals A obtained was 2. Proceeding to step (5), calculate k1And k2All values of (a) are 0.5.
Carrying out step (6), Kn-1=KnThis is true.
Proceeding to step (7), calculating the standard interval A1I.e. the value of the reference frequency and f in the 7 th to 10 th minutes1(ii) a Calculating a standard interval A2I.e. the sum f of the reference frequency values in minutes 3 to 62. f1 is given a weight value of k1，f2The corresponding weight value is k2。
And (5) carrying out the steps (8) to (12).
In the above method, step (2) further includes: after step (2) is completed, step (3.1) is performed; then, steps (4) to (9) are performed.
Step (3.1): removing the standard interval A by using the quantity value of part of the reference frequencies in the currently stored reference frequencies to obtain a quantity value n; the number n is the number of standard intervals A.
Storing the values of 300 reference frequencies; the frequency within 30 minutes after the prediction is needed is taken as an example.
Step (2) is carried out, and the numerical values of the prediction interval C and the standard interval A are set to be 30; the frequency within 30 minutes after the current time point is predicted.
Performing step (3.1), using part of the reference frequencies in the currently stored reference frequencies 300; can be 60, 90, 120, 150, 210, etc.; if the value of the partial reference frequency is 90; removing the numerical value of the standard interval A to obtain a numerical value 3 of n; the number of standard intervals A obtained was 3.
And (4) carrying out the step (4), wherein the value of n is more than or equal to 10 and more than or equal to 2.
And (4.1) judging that n is an integer.
Proceeding to step (5), calculate k1The value of (A) is 0.5; k is a radical of2The value of (A) is 0.25; are all k3The value of (A) is 0.25.
Carrying out step (6), Kn-1=KnThis is true.
Proceeding to step (7), calculating the standard interval A1I.e. the sum f of the values of the reference frequency in the 270 th to 300 th minutes1(ii) a Calculating a standard interval A2I.e. the sum f of the values of the reference frequency in the 240 th to 270 th minutes2(ii) a Calculating a standard interval A3I.e. the sum f of the reference frequency values in minutes 210 to 2403。f1The corresponding weight is k1，f2The corresponding weight value is k2，f3The corresponding weight value is k3。
And (5) carrying out the steps (8) to (12).
If the value of the partial reference frequency in the step (3.1) is 150; removing the value of the standard interval A to obtain a value 5 of n; the number of standard intervals A obtained was 5.
And (4) carrying out the step (4), wherein the value of n is more than or equal to 10 and more than or equal to 2.
And (4.1) judging that n is an integer.
Proceeding to step (5), calculate k1The value of (A) is 0.5; k is a radical of2The value of (A) is 0.25; are all k3The value of (A) is 0.125; k is a radical of4The value of (3) is 0.0625; k is a radical of5The value of (3) is 0.0625.
Carrying out step (6), Kn-1=KnThis is true.
Proceeding to step (7), calculating the standard interval A1I.e. the sum f of the values of the reference frequency in the 270 th to 300 th minutes1(ii) a Calculating a standard interval A2I.e. the sum f of the values of the reference frequency in the 240 th to 270 th minutes2(ii) a Calculating a standard interval A3I.e. the sum f of the reference frequency values in minutes 210 to 2403(ii) a Calculating a standard interval A4I.e. the sum f of the values of the reference frequency in the 180 th to 210 th minutes4(ii) a Calculating a standard interval A5I.e. the sum f of the reference frequency values in minutes 150 to 1805。f1The corresponding weight is k1，f2The corresponding weight value is k2，f3The corresponding weight value is k3；f4The corresponding weight value is k4；f5The corresponding weight value is k5。
In the above method, if the currently stored reference frequency value is 300; two standard intervals, three standard intervals, four standard intervals, five standard intervals, six standard intervals, seven standard intervals, eight standard intervals, nine standard intervals, or ten standard intervals may be used to respectively predict the frequency within 30 minutes after the current time point; the greater the number of standard intervals used; the higher the accuracy of the predicted frequency.

Claims (6)

1. A small hydropower frequency prediction method based on weighted distribution is characterized by comprising the following steps: comprises the following steps;
step (1), taking the last frequency of the last second in each minute of small hydropower station operation as the reference frequency per minute; recording the current numerical value of the reference frequency generated by the small hydropower station; respectively storing the value of each reference frequency generated every minute;
setting numerical values of a prediction interval C and a standard interval A; the prediction interval C is a time period of the frequency to be predicted within X minutes after the current time point; the standard interval a is a time period close to the time axis of the prediction interval C and having a reference frequency; the values of the prediction interval C, the standard interval A and the X are the same;
step (3), removing the standard interval A from the current numerical value of the reference frequency to obtain a numerical value n; the number n is the number of the standard interval A;
step (4), judging whether the numerical value n meets the condition that n is more than or equal to 10 and more than or equal to 2; if yes, performing the step (4.1); if not, the step (2) is carried out to reconfirm the C, A and the X values;
step (4.1) judging whether the numerical value n is an integer; if yes, performing the step (5); if not, taking an integer downwards from the numerical value n, and then performing the step (5);
step (5) passing a formula kn-1=10（1-n）*5（n-1）And formula k1+k2+kn-1+kn= 1; respectively calculating a corresponding weighted value K of each standard interval A;
step (6) judging kn-1=knWhether the result is true or not; if yes, performing the step (7); if not, the step (2) is carried out to reconfirm the C, A and the X values; k is a radical ofnThe weighting value corresponding to the farthest standard interval A from the time axis of the prediction interval C;
step (7), respectively calculating the numerical value and f of the reference frequency in each standard interval A; *
Step (8) by the formula F = k1*f1+k2*f2+kn-1*fn-1+ kn*fn(ii) a Calculating the total frequency F of the prediction interval;
step (9) by the formula E = F/a; the average frequency E of the prediction interval is calculated.
2. The small hydropower frequency prediction method based on weighted distribution as claimed in claim 1, wherein the method comprises the following steps: step (9) is followed by step (10) -step (12);
step (10), judging whether E is less than or equal to 49.5 Hz; if so, increasing the power of the small hydropower station; if not, performing the step (11);
step (11), judging whether E is greater than or equal to 50.2 Hz; if so, adjusting the power of the small hydropower station to be small; if not, performing the step (12);
and (12) not adjusting the power of the small hydropower station.
3. The small hydropower frequency prediction method based on weighted distribution as claimed in claim 1, wherein the method comprises the following steps: the values of C, A and X are greater than 1 and less than 30.
4. The small hydropower frequency prediction method based on weighted distribution as claimed in claim 1, wherein the method comprises the following steps: the step (1) further comprises: setting a threshold value of the reference frequency; if the current quantity value of the reference frequency is larger than the threshold value of the reference frequency; calculating a difference z between the current magnitude value of the reference frequency and a threshold value of the reference frequency; from the point in time furthest from the prediction interval C, the z reference frequencies are cleared.
5. The small hydropower frequency prediction method based on weighted distribution as claimed in claim 1, wherein the method comprises the following steps: after step (2) is completed, step (3.1) is performed; then carrying out steps (4) - (9);
step (3.1): removing the standard interval A by using the quantity value of part of the reference frequencies in the currently stored reference frequencies to obtain a quantity value n; the number n is the number of standard intervals A.
6. The small hydropower frequency prediction method based on weighted distribution as claimed in claim 1 or 5, wherein the method comprises the following steps: according to the time axis, the standard interval closest to the prediction interval C is the first standard interval A1(ii) a The standard interval farthest from the prediction interval C is the nth standard interval An(ii) a In the step (7), the first standard interval A is selected1The calculation of the value f of the reference frequency in each standard interval is started.
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WO2023109417A1 (en) * 2021-12-16 2023-06-22 广州城市理工学院 Weighted allocation-based frequency prediction method for small hydropower station

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CN113659565B (en) * 2021-07-19 2023-06-13 华北电力大学 Online prediction method for frequency situation of new energy power system
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