CN109447336A - Water level optimal control method between a kind of upper pond and its reregulating reservoir dam - Google Patents
Water level optimal control method between a kind of upper pond and its reregulating reservoir dam Download PDFInfo
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Abstract
The invention discloses water level optimal control methods between a kind of upper pond and its reregulating reservoir dam, based on the multiple neural networks of history data training, according to current working, it finds and the most similar Conditions Matching of history operating condition, corresponding neural network is selected, water level and the prediction of reregulating reservoir upstream water level behind the dam of library in realization；After Shang Kuba on water level and reregulating reservoir upstream water level fundamentals of forecasting, the above library storage outflow is variable, carries out water level optimal control tentative calculation between step reservoir, realizes that step gross capability is maximum.The present invention makes full use of step power station history data, solves the challenge of water level control between reregulating reservoir and upper library dam, water level can provide step power benefit between optimal control dam and provides solution route.
Description
Technical field
The present invention relates to water utilities computing technique fields, and in particular to water level between a kind of upper pond and its reregulating reservoir dam
Optimal control method.
Background technique
Relationship calibration sheet between reregulating reservoir upstream water level in step reservoir and higher level's reservoir storage outflow, tail water
It is a complicated knowledge question of water conservancy in matter.The relationship between this three is cleared, is theoretically needed to rely between step reservoir
The hydraulic characteristic(s) in river is using the methods of one-dimensional or two-dimentional hydraulic model Modeling Calculation.But in production practice, river is disconnected
Face data etc. lacks enough precision mostly, therefore often lacks practicability using hydraulic model calculated result, is more
Using artificial experience, rough quantitative predication is provided, constrains reservoir operation decision.
Summary of the invention
It is an object of the invention to overcome deficiency in the prior art, a kind of upper pond and its reregulating reservoir are provided
Water level optimal control method between dam solves the problems, such as often to lack practicability using hydraulic model calculated result.
In order to solve the above-mentioned technical problems, the present invention provides water level between a kind of upper pond and its reregulating reservoir dam is excellent
Change control method, characterized in that including following procedure:
S1 extracts four class of water level, section flow and reregulating reservoir upstream water level behind upper pond storage outflow, upper pond dam
The historical data of each influence factor in the same period is formed the historical data set of the period by the historical data of influence factor；
S2 clusters to divide the history data set of day part according to the water level interval division of reregulating reservoir upstream water level
It is assigned to corresponding water level section；
S3 constructs identical neural network model for each water level section, and the input of neural network model is upper pond at the beginning of the period
Water level, section flow, reregulating reservoir upstream water level at the beginning of the period, exported as the period behind storage outflow, upper pond dam at the beginning of the period
Water level and period end reregulating reservoir upstream water level behind last upper pond dam utilize the history data set training in each water level section
Belong to the neural network model in its own water level section；
S4 obtains upper pond storage outflow, upper pond upstream water level, section flow and reregulating reservoir dam at the beginning of this period
Preceding water level, determines the water level section belonging to it, obtains period end upper water by the Neural Network model predictive in this water level section
Water level and period end reregulating reservoir upstream water level behind the dam of library calculate this period of step reservoir gross capability；Constantly regulate upper water
Library storage outflow repeats neural network prediction, until this period of step reservoir gross capability is not further added by, it is corresponding at this time on
Water level and reregulating reservoir upstream water level are to control water level between optimal dam after trip reservoir dam.
Preferably, before being clustered to the history data set of day part, the history data set of day part is normalized.
Preferably, method for normalizing is that maximum-minimum sandards convert Various types of data to the criterion numeral between 0-1
According to.
Preferably, clustering method is K- mean cluster.
Preferably, when being clustered to the history data set of day part, with the historical data of Euclidean distance measurement day part
Similarity between collection.
Preferably, water level and water level, reregulating reservoir dam behind the upper pond dam of corresponding period will be controlled between optimal dam
The actual value of preceding water level compares, and records its deviation, finely tunes the prediction network with intensified learning method based on deviation.
Compared with prior art, the beneficial effects obtained by the present invention are as follows being:
1) clustering method in data mining is used to provide training sample appropriate for neural metwork training；
2) independent of fine river cross-section data, upper pond can be established according to history run data and anti-tune is water-saving
Flow, ga ge relation between library, and can be calculated, suitable for the water level between any step reservoir there are reregulating reservoir
Prediction and control；
3) intensified learning method is used, can constantly be mentioned according to predicted value and the deviation of measured value come re -training neural network
High precision of prediction, has self-learning capability.
Detailed description of the invention
Fig. 1 is the flow diagram of the method for the present invention；
Fig. 2 is the single Neural structure for water level forecast between dam；
Fig. 3 is that neural network updates flow chart in the method for the present invention.
Specific embodiment
The invention will be further described below in conjunction with the accompanying drawings.Following embodiment is only used for clearly illustrating the present invention
Technical solution, and not intended to limit the protection scope of the present invention.
Water level optimal control method between a kind of upper pond of the invention and its reregulating reservoir dam, including following procedure:
Step S1 extracts the historical data for influencing the influence factor of upper pond and its reregulating reservoir water level, by the same period
The historical data of interior each influence factor forms the historical data set of the period.
From reservoir operation database, the history of upper pond influence factor relevant to its reregulating reservoir water level is extracted
Data.Need to extract following 4 class historical datas: 1) upper pond storage outflow in embodiments of the present invention, 2) behind upper pond dam
Water level, 3) section flow, 4) reregulating reservoir upstream water level.
The data volume of this kind of historical data is related with the time granularity for acquiring data in reservoir operation database, and generally 5
Minute, 15 minutes or 1 hour, there may be differences for each reservoir time granularity, but do not influence the implementation of the method in the present invention.
Above-mentioned 4 class master data is required to unified Period Length, if it is different, then need using interpolation method, it is unified to minimum
Time granularity data format.Then will correspond to water level behind the upper pond storage outflow of same period, upper pond dam,
Section flow, reregulating reservoir upstream water level are combined into a data set, thus the historical data set D that tectonic style is unified.Such as
In certain period t, upper pond storage outflow is Q1t, water level is Z behind upper pond dam1t, section flow be Q2t, reregulating reservoir
Upstream water level is Z2t, then the history data set of the period is expressed as Dt=(Q1t,Z1t,Q2t,Z2t), remaining period analogizes, then n when
Historical data set D=(D of section1,D2,D3…Dn).
Reregulating reservoir upstream water level is divided into several sections by step S2, is carried out to the history data set of day part
Cluster is distributed to each water level section.
For the historical data set D arranged in step 1), using the clustering method in data digging method into
Row preliminary analysis improves the specific aim of subsequent calculating its object is to sort out scheduling operating condition similar in historical data,
The clustering method used is K- mean cluster.
K- mean cluster principle are as follows: the set of given n object, division methods are k subregion for constructing data,
In one race of each partitioned representation, give the k number of partitions to be constructed, division methods create an initial division first, then
Each sample is relocated using a kind of re-positioning technology of iteration, until meeting condition.
Specific step is as follows:
A) abnormal data that cleaning historical data is concentrated, abnormal data includes negative value, significantly more than data of normal range (NR) etc.；
B) history data set after cleaning is normalized, i.e., converts 0-1 for Various types of data using maximum-minimum sandards
Between normal data；
C) similarity between the history data set Dt of day part is measured, is measured with Euclidean distance；
D) reregulating reservoir upstream water level is divided into several sections as needed, with K- means clustering algorithm to day part
History data set carry out sample clustering, historical data is distributed to each ready-portioned water level section, as later neural network
Training sample data.
Step S3 constructs corresponding neural network for each water level section, and based on the history data set in each water level section
Close training neural network.
Potential rule in historical data is found using artificial intelligence approach, and prediction water is promoted by accumulation field data
It is flat.
The present invention finds water level, area behind upper pond storage outflow, upper pond dam using artificial neural network algorithm
Between nonlinearity relationship between flow and reregulating reservoir upstream water level, and make a prediction.
The cluster analysis result obtained using previous step is respectively trained as training sample corresponding to each water level section
Neural network, i.e. how many water level section just train how many a neural networks, are parallel relation between each neural network.
Neural network structure in each water level section is identical.Neural network structure input, output schematic diagram such as Fig. 2
Shown, input layer is 4 nodes: water level, section stream behind upper pond storage outflow, upper pond dam at the beginning of the period at the beginning of the period
Reregulating reservoir upstream water level at the beginning of amount, period.Output layer is 2 nodes: water level behind period end upper pond dam, and period end demodulates
Water-saving library upstream water level.Hidden layer node quantity is 2 times of input layer quantity.
Step S4, the above library storage outflow are variable, carry out water level optimal control tentative calculation between step reservoir, realize that step is total
Power output is maximum.
Under the background that the present invention inquires into, the main purpose of water level optimal control is between upper pond and its reregulating reservoir
Bigger step power benefit is obtained, as follows:
1) the upper pond upstream water level Zs at the beginning of this period, the upper pond dam at the beginning of this period are obtained from reservoir monitoring data
Water level Ze, this period section flow Q, reregulating reservoir upstream water level Zd at the beginning of this period afterwards；
2) selection and the most matched prediction neural network of current scheduling operating condition；
Conditions Matching mode is as follows:
A) water level, section after obtaining upper pond storage outflow, upper pond dam at the beginning of the period at the beginning of the period in reservoir monitoring data
Flow, reregulating reservoir upstream water level at the beginning of the period form current step reservoir floor data collection, building form and phase in step S1
Together, the discriminant parameter as step reservoir operating condition；
B) normal data between 0-1 is converted Various types of data to using min-max standardization；
C) each cluster data in current step reservoir floor data collection and step S2 data mining results is calculated separately to concentrate in the heart
Distance, measured, found out with current working apart from immediate data set as matched data collection with Euclidean distance；
D) according to the water level section where matched data collection, corresponding prediction neural network is matched；
3) a upper pond storage outflow Qt is assumed, with water level Ze, this when behind the upper pond dam at the beginning of this period in step 1)
Section section flow Q, the lower reservoir upstream water level Zd at the beginning of this period obtain matched neural network as input, using previous step
Water level Z1 and reregulating reservoir upstream water level Z2 behind the upper pond dam at calculation interval end；
4) based on essential informations such as Qt, Zs, Ze, Zd, Z1, this period of upper pond power output N1, base are calculated by hydroelectric generation principle
Reregulating reservoir power output N2 is calculated using upstream water level ~ power output relation curve of reregulating reservoir in Z2；It is total to calculate step reservoir
Contribute N1+N2；
5) step 3) is returned to, by the storage outflow of fixed step size adjustment upper pond, tentative calculation optimizing can be carried out by dichotomy, directly
It is not further added by this period of step reservoir gross capability, upper library storage outflow at this time is Qopt.At this time behind corresponding upper pond dam
Water level and reregulating reservoir upstream water level are to control water level between optimal dam.
Step S5, neural network automatically update.
After step S4 is finished, record is corresponding to water level, counter regulation behind the period of control water level, upstream dam between optimal dam
Water level value and used prediction neural network before reservoir dam.It is water-saving with water level, anti-tune behind the upper pond dam of corresponding period again
The actual value of library upstream water level compares, input item when recording its deviation, and the deviation being pressed the place period corresponding to prediction
Part, and then the prediction network is finely tuned with intensified learning method based on deviation, it can reach the mesh for promoting the prediction network effect
's.
The present invention utilizes data mining and artificial intelligence approach, and upper pond is obtained from a large amount of history data and is gone out
Associated potential rule between water level, reregulating reservoir upstream water level behind library flow, upper pond dam has extensive be applicable in
Property, as long as there is certain history data support, and prediction can be constantly promoted with the accumulation of operation data
Accuracy.
It should be understood by those skilled in the art that, embodiments herein can provide as method, system or computer program
Product.Therefore, complete hardware embodiment, complete software embodiment or reality combining software and hardware aspects can be used in the application
Apply the form of example.Moreover, it wherein includes the computer of computer usable program code that the application, which can be used in one or more,
The computer program implemented in usable storage medium (including but not limited to magnetic disk storage, CD-ROM, optical memory etc.) produces
The form of product.
The application is referring to method, the process of equipment (system) and computer program product according to the embodiment of the present application
Figure and/or block diagram describe.It should be understood that every one stream in flowchart and/or the block diagram can be realized by computer program instructions
The combination of process and/or box in journey and/or box and flowchart and/or the block diagram.It can provide these computer programs
Instruct the processor of general purpose computer, special purpose computer, Embedded Processor or other programmable data processing devices to produce
A raw machine, so that being generated by the instruction that computer or the processor of other programmable data processing devices execute for real
The device for the function of being specified in present one or more flows of the flowchart and/or one or more blocks of the block diagram.
These computer program instructions, which may also be stored in, is able to guide computer or other programmable data processing devices with spy
Determine in the computer-readable memory that mode works, so that it includes referring to that instruction stored in the computer readable memory, which generates,
Enable the manufacture of device, the command device realize in one box of one or more flows of the flowchart and/or block diagram or
The function of being specified in multiple boxes.
These computer program instructions also can be loaded onto a computer or other programmable data processing device, so that counting
Series of operation steps are executed on calculation machine or other programmable devices to generate computer implemented processing, thus in computer or
The instruction executed on other programmable devices is provided for realizing in one or more flows of the flowchart and/or block diagram one
The step of function of being specified in a box or multiple boxes.
The above is only a preferred embodiment of the present invention, it is noted that for the ordinary skill people of the art
For member, without departing from the technical principles of the invention, several improvements and modifications, these improvements and modifications can also be made
Also it should be regarded as protection scope of the present invention.
Claims (6)
1. water level optimal control method between a kind of upper pond and its reregulating reservoir dam, characterized in that including following procedure:
S1 extracts four class of water level, section flow and reregulating reservoir upstream water level behind upper pond storage outflow, upper pond dam
The historical data of each influence factor in the same period is formed the historical data set of the period by the historical data of influence factor；
S2 clusters to divide the history data set of day part according to the water level interval division of reregulating reservoir upstream water level
It is assigned to corresponding water level section；
S3 constructs identical neural network model for each water level section, and the input of neural network model is upper pond at the beginning of the period
Water level, section flow, reregulating reservoir upstream water level at the beginning of the period, exported as the period behind storage outflow, upper pond dam at the beginning of the period
Water level and period end reregulating reservoir upstream water level behind last upper pond dam utilize the history data set training in each water level section
Belong to the neural network model in its own water level section；
S4 obtains upper pond storage outflow, upper pond upstream water level, section flow and reregulating reservoir dam at the beginning of this period
Preceding water level, determines the water level section belonging to it, obtains period end upper water by the Neural Network model predictive in this water level section
Water level and period end reregulating reservoir upstream water level behind the dam of library calculate this period of step reservoir gross capability；Constantly regulate upper water
Library storage outflow repeats neural network prediction, until this period of step reservoir gross capability is not further added by, it is corresponding at this time on
Water level and reregulating reservoir upstream water level are to control water level between optimal dam after trip reservoir dam.
2. water level optimal control method between a kind of upper pond according to claim 1 and its reregulating reservoir dam, special
Sign is, before clustering to the history data set of day part, the history data set of day part is normalized.
3. water level optimal control method between a kind of upper pond according to claim 2 and its reregulating reservoir dam, special
Sign is that method for normalizing is that maximum-minimum sandards convert Various types of data to the normal data between 0-1.
4. water level optimal control method between a kind of upper pond according to claim 1 and its reregulating reservoir dam, special
Sign is that clustering method is K- mean cluster.
5. water level optimal control method between a kind of upper pond according to claim 1 and its reregulating reservoir dam, special
Sign is, when clustering to the history data set of day part, with similar between the history data set of Euclidean distance measurement day part
Degree.
6. water level optimal control method between a kind of upper pond according to claim 1 and its reregulating reservoir dam, special
Sign is that the reality of water level and water level, reregulating reservoir upstream water level behind the upper pond dam of corresponding period will be controlled between optimal dam
Actual value compares, and records its deviation, finely tunes the prediction network with intensified learning method based on deviation.
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| CN114169536A (en) * | 2022-02-11 | 2022-03-11 | 希望知舟技术(深圳)有限公司 | Data management and control method and related device |
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