CN114861808A - Variable load test data intelligent sorting method for hydroelectric generating set - Google Patents
Variable load test data intelligent sorting method for hydroelectric generating set Download PDFInfo
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Abstract
An intelligent sorting method for variable load test data of a hydroelectric generating set comprises the following steps: selecting signals for judging stable working conditions, wherein the signals at least comprise active power P, the other signals are selectable signals, setting the maximum allowable fluctuation amount of each signal in the stable working conditions, and setting the length t of a stable interval; continuously traversing the data of the whole test duration by taking the sampling time as a step length, and solving the difference value between the maximum value and the minimum value of each parameter in each time window during each traversal; stability index s of each stable interval; the m stable interval sections are classified according to the active power value, the classification principle is that values with similar active power are classified into the same class, and finally k classes are obtained; and finding out the time interval with the minimum s value in each class as the finally screened stable operation interval. The method can realize the automatic screening of the steady-state working condition data in the variable load test process, can accurately identify the steady-state working condition, and is suitable for large-batch data sorting.
Description
Technical Field
The invention belongs to the field of variable load test data processing of a hydroelectric generating set, and particularly relates to an intelligent sorting method for variable load test data of the hydroelectric generating set.
Background
The variable load test of the hydroelectric generating set needs to adjust the test working conditions, the test is carried out under each working condition for a period of time, then the test is changed to the next working condition, the test is carried out for a period of time, and the test is repeated in sequence until all the test working conditions are completely covered. The test data is continuously collected, namely the whole variable working condition test process is collecting data, and during data analysis, generally only the steady-state working condition with fixed time length needs to be analyzed, so that a plurality of steady time sections with fixed time length need to be sorted out from the collected whole continuous signal, and only one steady time section is sorted out nearby the same working condition (namely the same active power value), so that the whole continuous time length can be divided into a plurality of time sections for subsequent data processing and analysis.
The traditional method generally adopts manual identification, namely, the oscillogram of active power in a signal is observed manually, when the value of the active power is stable and unchanged (or changes slightly), the stable working condition is determined, and then the time period is sorted and selected manually.
The traditional method is low in efficiency, the sorted waveform interval is not necessarily the interval with the minimum active power fluctuation, meanwhile, the stable working condition is judged only by one active power signal, complex screening conditions cannot be realized, the stability of the active power cannot completely represent the stable working condition, when the active power is stable and other signals (such as flow signals) are not stable, the process actually belongs to a transition process, and if the process is considered as the stable working condition, certain influence is caused on subsequent data analysis.
Disclosure of Invention
In view of the technical problems in the background art, the intelligent sorting method for the variable load test data of the water turbine generator set can automatically find out a time interval with optimal stability near the same active power value; can carry out autofilter to big batch data, the computational efficiency is high.
In order to solve the technical problems, the invention adopts the following technical scheme to realize:
an intelligent sorting method for variable load test data of a hydroelectric generating set comprises the following steps:
s1: selecting a signal for judging a stable working condition, wherein the signal at least comprises active power P, other signals are selectable signals, and the code number of the selectable signal is X 1 、X 2 ...X n (ii) a n is a natural positive integer;
s2: setting the maximum allowable fluctuation amount of each signal under a stable working condition, wherein if the signal only has active power P, the maximum allowable fluctuation amount is MP; if the signal has a selectable signal, the maximum allowable fluctuation amount is MP and MX 1 、MX 2 ...MX n ；
S3: setting the length t of the stable interval section;
s4: starting from the time 0, continuously traversing the data of the whole test duration by using a time window with the length of t and the sampling time as a step length, and solving the difference value between the maximum value and the minimum value of each parameter in each time window during each traversal; if the signal only has active power P, the difference value between the maximum value and the minimum value is delta P; if the signal has a selectable signal, the difference between the maximum value and the minimum value is Δ P and Δ X respectively 1 、ΔX 2 ...ΔX n ；
If the signal only has active power P, when delta P is satisfied<M P That is, the time interval is considered as a stable operation interval meeting the requirement, and the time intervals meeting the requirement are numbered as t 1 ,t 2 ...t m (ii) a m is a natural positive integer;
if the signal has a selectable signal, Δ P is satisfied simultaneously<M P ，ΔX 1 <MX 1 ，ΔX 2 <MX 2 ...ΔX n <MX n When the time interval is considered to be a stable operation interval meeting the requirement, all the time intervals meeting the requirement are numbered as t in sequence 1 ,t 2 ...t m (ii) a m is a natural positive integer;
s5: calculating the stability index s of each stable interval obtained in the step 4;
s6: classifying the m stable intervals obtained in the step 4 according to an active power value, wherein the classification principle is that values with similar active power are classified into the same class, and finally obtaining k classes; no matter how kinds of signals exist, the signals are classified according to active power;
s7: and finding out the time interval with the minimum s value in each class from the k classes to be used as the finally screened stable operation interval, thus obtaining k time intervals with the best stability indexes meeting the conditions to be used as the finally output sorting results T1 and T2 … … Tk.
In a preferred embodiment, the calculation method in step S5 is as follows:
s5.1: the maximum value of each parameter over the entire test duration is read,
if the signal only has active power P, the maximum value is PMax;
if the signal has a selectable signal, its value is PMax, X in sequence 1 Max、X 2 Max...X n Max；
S5.2: and (3) solving the normalized index of the fluctuation value of each parameter in each time interval meeting the condition:
sP=ΔP/PMax，sX 1 =ΔX 1 /X 1 Max，sX 2 =ΔX 2 /X 2 Max...sX n =ΔX n /X n Max；
s5.3: obtaining the stability index s = sP + sX of each stable interval obtained in the step 4 1 +sX 2 +...sX n 。
In a preferred embodiment, in step S6, the average value PAVG of the active power in each stable interval obtained in step S4 is calculated, and the m stable intervals obtained in step S4 are classified according to the value PAVG by using a DBSCAN density clustering algorithm, so as to obtain k classes.
This patent can reach following beneficial effect:
1. the programmable degree automatic operation has high calculation efficiency and good accuracy. The method can simultaneously consider the identification of a plurality of parameters to the stable operation working condition, can automatically realize the automatic screening of the stable working condition data in the variable load test process through a program, has strong identification accuracy and high efficiency, is suitable for sorting large-batch data, can self-define the identification parameters of the stable working condition, and can simultaneously consider the identification of a plurality of parameters to the stable operation working condition;
2. a time interval with optimal stability near the same active power value can be automatically found out; can carry out autofilter to big batch data, the computational efficiency is high.
Drawings
The invention is further illustrated by the following examples in conjunction with the accompanying drawings:
FIG. 1 is a schematic diagram of the data sorting results of the present invention;
FIG. 2 is a schematic diagram of the present invention in a continuous traversal.
Detailed Description
A preferred scheme is as shown in fig. 1 to fig. 2, and the intelligent sorting method for variable load test data of the hydroelectric generating set comprises the following steps:
s1: selecting a signal for judging a stable working condition, wherein the signal at least comprises active power P, other signals are selectable signals, and the code number of the selectable signal is X 1 、X 2 ...X n (ii) a n is a natural positive integer;
s2: setting the maximum allowable fluctuation amount of each signal under a stable working condition, wherein if the signal only has active power P, the maximum allowable fluctuation amount is MP; if the signal has a selectable signal, the maximum allowable fluctuation amount is MP and MX 1 、MX 2 ...MX n ；
S3: setting the length t of the stable interval section;
s4: starting from the time 0, continuously traversing the data of the whole test time length by using a time window with the length of t and the sampling time as a step length, and solving the maximum value and the minimum value of each parameter in each time window during each traversalA difference value; if the signal only has active power P, the difference value between the maximum value and the minimum value is delta P; if the signal has a selectable signal, the difference between the maximum value and the minimum value is Δ P and Δ X respectively 1 、ΔX 2 ...ΔX n ；
If the signal only has active power P, when delta P is satisfied simultaneously<M P That is, the time interval is considered as a stable operation interval meeting the requirement, and the time intervals meeting the requirement are numbered as t 1 ,t 2 ...t m (ii) a m is a natural positive integer;
if the signal has a selectable signal, Δ P is satisfied simultaneously<M P ，ΔX 1 <MX 1 ，ΔX 2 <MX 2 ...ΔX n <MX n When the time interval is considered to be a stable operation interval meeting the requirement, all the time intervals meeting the requirement are numbered as t in sequence 1 ,t 2 ...t m (ii) a m is a natural positive integer;
s5: calculating the stability index s of each stable interval obtained in the step 4;
the calculation method in step S5 is as follows:
s5.1: the maximum value of each parameter over the entire test duration is read,
if the signal only has active power P, the maximum value is PMax;
if the signal has a selectable signal, the value is PMax, X in sequence 1 Max、X 2 Max...X n Max；
S5.2: and (3) solving the normalized index of the fluctuation value of each parameter in each time interval meeting the condition:
sP=ΔP/PMax，sX 1 =ΔX 1 /X 1 Max，sX 2 =ΔX 2 /X 2 Max...sX n =ΔX n /X n Max；
s5.3: obtaining the stability index s = sP + sX of each stable interval obtained in the step 4 1 +sX 2 +...sX n 。
S6: classifying the m stable interval sections obtained in the step 4 according to the active power value, wherein the classification principle is that values with similar active power are classified into the same class, and finally k classes are obtained; no matter how kinds of signals exist, the signals are classified according to active power;
in step S6, the average value PAVG of the active power in each stable interval obtained in step S4 is calculated, and the m stable intervals obtained in step S4 are classified according to the value PAVG by using the DBSCAN density clustering algorithm, so as to obtain k classes.
S7: and finding out the time interval with the minimum s value in each class from the k classes to be used as the finally screened stable operation interval, thus obtaining k time intervals with the best stability indexes meeting the conditions to be used as the finally output sorting results T1 and T2 … … Tk.
Example 1:
the following example is explained when the selectable signal is the flow rate Q:
step 1, selecting signal active power P and flow Q for judging stable working conditions.
Step 2, setting the maximum allowable fluctuation quantity MP =5MW and MQ =10m of each signal under the stable working condition 3 /s。
And 3, setting the length t of the stable interval section. t is taken to be 30 s.
And 4, starting from the time 0, continuously traversing the data of the whole test time length by using a time window with the length of t and the sampling time as a step length, and solving the difference value between the maximum value and the minimum value of each parameter in each time window, namely delta P and delta Q (for example, a certain interval is delta P =4MW, and delta Q =6 m) during each traversal 3 S) when Δ P is satisfied at the same time<MP，ΔQ<During MQ, the time interval is considered as a stable operation interval meeting the requirement, and all the time intervals meeting the requirement are numbered as t in sequence 1 ,t 2 ...t m 。
And 5, calculating the stability index s of each stable interval section obtained in the step 4, wherein the calculation method comprises the following steps:
step 5.1, reading the maximum value of each parameter in the whole test time length range, wherein the maximum value is PMax =800MW and QMax =600m in sequence 3 /s；
Step 5.2, obtaining the normalization index of the fluctuation value of each parameter in each time interval meeting the condition
sP = Δ P/PMax =0.005, sQ = Δ Q/QMax =0.01 (taking the interval exemplified in step 4 as an example);
step 5.3, the stability index s = sP + sQ =0.015 (taking the interval illustrated in step 4 as an example) of each stable interval obtained in step 4 is obtained.
And 6, classifying the m stable interval sections obtained in the step 4 according to the active power value, wherein the classification principle is that values with similar active power are classified into the same class, and the specific implementation method is as follows:
calculating the average value PAVG =700MW of the active power in each stable interval obtained in the step 4 (taking the interval exemplified in the step 4 as an example), and classifying the m stable intervals obtained in the step 4 according to the PAVG value by using a DBSCAN density clustering algorithm to obtain k classes.
And 7, finding out the time interval with the minimum s value in each class in the k classes obtained in the step 6, and taking the time interval as the finally screened stable operation interval, so that k time intervals with the best stability indexes meeting the conditions can be obtained and taken as finally output sorting results T1 and T2 … … Tk.
The above-described embodiments are merely preferred embodiments of the present invention, and should not be construed as limiting the present invention, and the scope of the present invention is defined by the claims, and equivalents including technical features described in the claims. I.e., equivalent alterations and modifications within the scope hereof, are also intended to be within the scope of the invention.
Claims (3)
1. The intelligent sorting method for the variable load test data of the hydroelectric generating set is characterized by comprising the following steps of:
s1: selecting a signal for judging a stable working condition, wherein the signal at least comprises active power P, other signals are selectable signals, and the code number of the selectable signal is X 1 、X 2 ...X n (ii) a n is a natural positive integer;
s2: setting the maximum allowable fluctuation amount of each signal under stable working condition, and if the signal only has active power PIf yes, the maximum allowable fluctuation amount is MP; if the signal has a selective signal, the maximum allowable fluctuation amount is MP and MX 1 、MX 2 ...MX n ；
S3: setting the length t of the stable interval section;
s4: starting from the time 0, continuously traversing the data of the whole test duration by using a time window with the length of t and the sampling time as a step length, and solving the difference value between the maximum value and the minimum value of each parameter in each time window during each traversal; if the signal only has active power P, the difference value between the maximum value and the minimum value is delta P; if the signal has a selectable signal, the difference between the maximum value and the minimum value is Δ P and Δ X respectively 1 、ΔX 2 ...ΔX n ；
If the signal only has active power P, when delta P is satisfied<M P That is, the time interval is considered as a stable operation interval meeting the requirement, and the time intervals meeting the requirement are numbered as t 1 ,t 2 ...t m (ii) a m is a natural positive integer;
if the signal has a selectable signal, Δ P is satisfied simultaneously<M P ，ΔX 1 <MX 1 ，ΔX 2 <MX 2 ...ΔX n <MX n When the time interval is considered to be a stable operation interval meeting the requirement, all the time intervals meeting the requirement are numbered as t in sequence 1 ,t 2 ...t m (ii) a m is a natural positive integer;
s5: calculating the stability index s of each stable interval obtained in the step 4;
s6: classifying the m stable interval sections obtained in the step 4 according to the active power value, wherein the classification principle is that values with similar active power are classified into the same class, and finally k classes are obtained;
s7: and finding out the time interval with the minimum s value in each class from the k classes to be used as the finally screened stable operation interval, thus obtaining k time intervals with the best stability indexes meeting the conditions to be used as the finally output sorting results T1 and T2 … … Tk.
2. The intelligent sorting method for the variable load test data of the hydroelectric generating set according to claim 1, characterized in that: the calculation method in step S5 is as follows:
s5.1: the maximum value of each parameter over the entire test duration is read,
if the signal only has active power P, the maximum value is PMax;
if the signal has a selectable signal, the value is PMax, X in sequence 1 Max、X 2 Max...X n Max；
S5.2: and (3) solving the normalized index of the fluctuation value of each parameter in each time interval meeting the condition:
sP=ΔP/PMax，sX 1 =ΔX 1 /X 1 Max，sX 2 =ΔX 2 /X 2 Max...sX n =ΔX n /X n Max；
s5.3: obtaining the stability index s = sP + sX of each stable interval obtained in the step 4 1 +sX 2 +...sX n 。
3. The intelligent sorting method for the variable load test data of the hydroelectric generating set according to claim 1, characterized in that: in step S6, the average value PAVG of the active power in each stable interval obtained in step S4 is calculated, and the m stable intervals obtained in step S4 are classified according to the value PAVG by using the DBSCAN density clustering algorithm, so as to obtain k classes.
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