CN113988481B - Wind power prediction method based on dynamic matrix prediction control - Google Patents

Wind power prediction method based on dynamic matrix prediction control Download PDF

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CN113988481B
CN113988481B CN202111585752.5A CN202111585752A CN113988481B CN 113988481 B CN113988481 B CN 113988481B CN 202111585752 A CN202111585752 A CN 202111585752A CN 113988481 B CN113988481 B CN 113988481B
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周鹏
孙礼豹
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Nanjing Naiweixin Information Technology Co ltd
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Abstract

The invention discloses a wind power prediction method based on dynamic matrix prediction control, which adopts more than two initial wind power prediction submodels, then adopts a first dynamic matrix prediction control system to correct the predicted values of the more than two initial wind power prediction submodels, secondly dynamically determines the weight of the predicted value of the initial wind power prediction submodel through an echo state neural network, and finally adopts a second dynamic matrix prediction control system to correct the predicted value of the echo state neural network, and then utilizes the dynamic matrix prediction control.

Description

Wind power prediction method based on dynamic matrix prediction control
Technical Field
The invention relates to a wind power prediction method based on dynamic matrix prediction control, and belongs to the technical field of wind power prediction.
Background
The wind power prediction is that a prediction model of wind power plant output power is established by using data of historical power, historical wind speed, landform, numerical weather forecast, wind turbine generator operating state and the like of a wind power plant, the data of the wind speed, the power, the numerical weather forecast and the like are used as input of the model, and the future active power of the wind power plant is predicted by combining the equipment state and the operating condition of the wind power plant generator.
The wind power prediction system has important significance for the operation of a power system connected with a large amount of wind power. The power system is a complex dynamic system, and it is the responsibility of the power grid to maintain the power balance among power generation, power transmission and power utilization. Without a wind power system, the power grid dispatching mechanism can make a power generation plan according to the daily load curve, so that the demand of the next day power is met. The output power of the wind power plant has volatility and intermittency, the difficulty of making a power generation plan is greatly increased due to large-scale access of wind power, and the wind power brings huge challenges to the dispatching and operation of a power system. The method is one of effective means for relieving peak load and frequency modulation pressure of the power system and improving wind power receiving capacity, meanwhile, wind power plant development enterprises can also reasonably arrange the maintenance of wind turbine equipment by selecting weather with smaller wind power by using wind power forecast, and the generated energy loss caused by the fact that the wind turbine cannot generate power is reduced as far as possible.
The existing wind power prediction methods can be mainly divided into methods such as a physical model method, a statistical method, a learning method and the like, but the methods all consider the wind power as a stable time sequence, and are difficult to predict the power mutation phenomenon caused by mutation events such as fan blade icing caused by strong wind shutdown and temperature reduction, so the wind power prediction accuracy is greatly reduced under extreme disaster conditions. In order to solve the problems, a relatively wide idea is to adopt a combined prediction model, so that the problem that a single model has a large prediction point error is overcome, but the combined prediction model is particularly important for determining the weight coefficient of each sub-model, the existing methods include an equal weight average method, a minimum variance method, an unconstrained least square method and the like, the weight coefficient of the combined prediction mode is mostly kept unchanged, and the prediction accuracy is limited when the combined prediction mode meets an uncertain weather state. Other methods for determining the weight coefficient include a probabilistic weight method, an information entropy method, a wavelet decomposition, a genetic algorithm, a neural network, and the like, and although these methods can achieve dynamic adjustment of the weight of each submodel and also achieve wind power prediction accuracy, the wind power prediction accuracy depends on the construction of the submodel, and is also limited in the case of an uncertain weather state.
Disclosure of Invention
The purpose of the invention is as follows: in order to overcome the defects in the prior art, the invention provides the wind power prediction method based on dynamic matrix prediction control, which has high accuracy and high calculation speed.
The technical scheme is as follows: in order to achieve the purpose, the invention adopts the technical scheme that:
a wind power prediction method based on dynamic matrix predictive control comprises the following steps:
step 1, collecting historical wind speed, topographic features, numerical weather forecast and historical wind power of a wind power plant as a historical data set, and dividing the historical data set into an initial wind power historical prediction data set and an echo state neural network historical prediction data set.
And 2, establishing more than two initial wind power prediction submodels according to the historical prediction data set of the initial wind power. Obtaining a prediction data set according to the history of the initial wind power
Figure 155303DEST_PATH_IMAGE001
At the first moment
Figure 250167DEST_PATH_IMAGE002
Wind power prediction value of initial wind power prediction submodel
Figure 385482DEST_PATH_IMAGE003
，
Figure 681334DEST_PATH_IMAGE004
，
Figure 902100DEST_PATH_IMAGE005
Representing the number of sub-models of the initial wind power predictor.
Step 3, the product obtained in the step 2 is processed
Figure 269496DEST_PATH_IMAGE001
At the first moment
Figure 728159DEST_PATH_IMAGE002
Wind power prediction value of initial wind power prediction submodel
Figure 257230DEST_PATH_IMAGE003
And detected
Figure 965292DEST_PATH_IMAGE001
Actual wind power at time of day
Figure 808483DEST_PATH_IMAGE006
Comparing to obtain the prediction error of the sub-model of the initial wind power predictor
Figure 121652DEST_PATH_IMAGE007
，
Figure 352782DEST_PATH_IMAGE008
。
Step 4, predicting errors of the initial wind power prediction submodels obtained in the step 4 through a first dynamic matrix prediction control system
Figure 548140DEST_PATH_IMAGE007
Feeding back the wind power predicted value of the initial wind power prediction submodel, and carrying out feedback correction on the wind power predicted value of the initial wind power prediction submodel to obtain a wind power prediction correction value of the initial wind power prediction submodel
Figure 929443DEST_PATH_IMAGE009
And further obtaining a wind power prediction correction matrix of the initial wind power prediction submodel
Figure 159436DEST_PATH_IMAGE010
，
Figure 764730DEST_PATH_IMAGE011
Indicating the prediction time.
And 5, constructing an echo state neural network, wherein the echo state neural network comprises an input layer, a reserve pool and an output layer which are sequentially connected.
During training, inputting the wind power prediction correction matrix of the initial wind power prediction submodel obtained in the step 4 into an echo state neural network, and training the echo state neural network, wherein a least square method is adopted to estimate a connection weight matrix from the neuron in the reserve pool to the neuron in the output layer
Figure 181805DEST_PATH_IMAGE012
And obtaining the trained echo state neural network.
And 6, processing the historical prediction data set of the echo state neural network through the steps 2 to 4 to obtain an echo state neural network correction set. Inputting the obtained echo state neural network correction set into the trainedObtaining the predicted value of the neural network in the echo state
Figure 186974DEST_PATH_IMAGE013
。
Step 7, the echo state neural network predicted value obtained in the step 6 is used
Figure 5894DEST_PATH_IMAGE013
And detected
Figure 782089DEST_PATH_IMAGE001
Actual wind power at time
Figure 952039DEST_PATH_IMAGE006
Comparing to obtain wind power prediction error
Figure 471882DEST_PATH_IMAGE014
，
Figure 145309DEST_PATH_IMAGE015
。
Step 8, the wind power prediction error obtained in the step 7 is subjected to prediction by a second dynamic matrix prediction control system
Figure 357984DEST_PATH_IMAGE014
Is fed back to
Figure 484072DEST_PATH_IMAGE001
Neural network prediction of echo state at time, pair
Figure 807606DEST_PATH_IMAGE001
The wind power predicted value of the echo state neural network predicted value at the moment is subjected to feedback correction to obtain the wind power predicted value after the second correction
Figure 601119DEST_PATH_IMAGE001
Temporal echo state neural network prediction correction
Figure 437225DEST_PATH_IMAGE016
。
Preferably: the step input of the first dynamic matrix predictive control system is the prediction error of the initial wind power predictor sub-model, and the sampling data of the corresponding wind power unit step response is obtained
Figure 50609DEST_PATH_IMAGE017
，
Figure 177834DEST_PATH_IMAGE018
，
Figure 825853DEST_PATH_IMAGE019
Modeling a time domain for a step response prediction model of a first dynamic matrix predictive control system, the step response prediction model of the first dynamic matrix predictive control system:
Figure 114752DEST_PATH_IMAGE020
wherein the content of the first and second substances,
Figure 481011DEST_PATH_IMAGE021
is at the same time
Figure 693818DEST_PATH_IMAGE022
Time of day prediction future
Figure 196344DEST_PATH_IMAGE023
At a moment in time have
Figure 921723DEST_PATH_IMAGE024
A continuous control increment
Figure 509699DEST_PATH_IMAGE025
，
Figure 978727DEST_PATH_IMAGE026
，… ，
Figure 601338DEST_PATH_IMAGE027
Wind power prediction under influenceThe output of the value is carried out,
Figure 684569DEST_PATH_IMAGE024
to control the time domain length.
Figure 25421DEST_PATH_IMAGE028
Is at the same time
Figure 580030DEST_PATH_IMAGE022
Time of day prediction future
Figure 57148DEST_PATH_IMAGE023
Outputting the wind power predicted value under the action of no control increment at each moment,
Figure 858750DEST_PATH_IMAGE029
is a dynamic matrix whose elements are step response coefficients describing the dynamic behavior of the system,
Figure 844872DEST_PATH_IMAGE030
for the future from now on
Figure 858965DEST_PATH_IMAGE024
The control variable at each instant.
In that
Figure 190589DEST_PATH_IMAGE022
Wind power predicted at time without control increment
Figure 694251DEST_PATH_IMAGE025
Under the action of
Figure 380667DEST_PATH_IMAGE019
The output predicted value at each time is
Figure 277079DEST_PATH_IMAGE031
。
In that
Figure 728789DEST_PATH_IMAGE022
Predicted wind power at the momentWith control increments
Figure 872194DEST_PATH_IMAGE025
Under the action of
Figure 409355DEST_PATH_IMAGE019
The output predicted value at each time is
Figure 561987DEST_PATH_IMAGE032
。
Preferably: the feedback correction of the first dynamic matrix predictive control system is as follows: according to
Figure 602624DEST_PATH_IMAGE033
Constantly exerting control action on wind power value
Figure 205948DEST_PATH_IMAGE034
Then, at
Figure 230404DEST_PATH_IMAGE035
The actual output is collected at all times
Figure 921149DEST_PATH_IMAGE036
And
Figure 81872DEST_PATH_IMAGE033
output prediction value of step response prediction model based on first dynamic matrix prediction control system at moment
Figure 832659DEST_PATH_IMAGE037
Comparing to obtain real-time prediction error
Figure 78832DEST_PATH_IMAGE038
：
Figure 573268DEST_PATH_IMAGE039
By predicting the error in real time
Figure 854076DEST_PATH_IMAGE038
The weighting modifies the prediction of the future output, i.e.:
Figure 775765DEST_PATH_IMAGE040
in the formula (I), the compound is shown in the specification,
Figure 509234DEST_PATH_IMAGE041
is composed of
Figure 541781DEST_PATH_IMAGE042
Predicted future of time error corrected
Figure 677097DEST_PATH_IMAGE019
The predicted wind power at each moment in time,
Figure 100002_DEST_PATH_IMAGE043
is an error correction vector, i.e. a weight coefficient applied when error correcting the predicted values at different time instants, wherein,
Figure 159899DEST_PATH_IMAGE044
。
preferably: the optimized performance indexes of the first dynamic matrix predictive control system are as follows:
Figure 115086DEST_PATH_IMAGE045
wherein the content of the first and second substances,
Figure 951324DEST_PATH_IMAGE046
the performance index of the optimization is shown,
Figure 206725DEST_PATH_IMAGE047
a weighting coefficient representing a tracking error of the optical disc,
Figure 470216DEST_PATH_IMAGE048
show in the future
Figure 912698DEST_PATH_IMAGE049
The desired value of the output wind power is rolled in time,
Figure 552627DEST_PATH_IMAGE050
a weighting factor representing a control increment.
Preferably: the input layer: comprises that
Figure 662534DEST_PATH_IMAGE024
And an input neuron.
A reserve pool: is composed of
Figure 96927DEST_PATH_IMAGE019
Sparse network formed by connecting individual neurons, and weight matrix for connection between neurons in reserve pool
Figure 682498DEST_PATH_IMAGE051
It is shown that,
Figure 594959DEST_PATH_IMAGE052
representing neurons within the reservoir
Figure 808640DEST_PATH_IMAGE053
And neurons
Figure 413934DEST_PATH_IMAGE054
Connection weight, connection weight matrix
Figure 96588DEST_PATH_IMAGE051
The connection weight between the neuron in the input layer and the neuron in the dynamic reserve pool is expressed by a matrix
Figure 812740DEST_PATH_IMAGE055
And (4) showing. The pool accepts inputs in two directions, one from the input layer and the other from the state of the previous output layer.
An output layer: comprises 1 output neuron, a matrix for connecting weights from neuron in reserve pool to neuron in output layer
Figure 631661DEST_PATH_IMAGE012
Representing a matrix of connection weights from neurons within the output layer to neurons within the reservoir
Figure 431293DEST_PATH_IMAGE056
And (4) showing.
The echo state neural network is
Figure 335664DEST_PATH_IMAGE001
The values at the time are expressed as follows:
Figure 573616DEST_PATH_IMAGE057
Figure 981464DEST_PATH_IMAGE058
Figure 397402DEST_PATH_IMAGE059
wherein the content of the first and second substances,
Figure 789069DEST_PATH_IMAGE060
is shown in
Figure 581444DEST_PATH_IMAGE001
The input layer input is made at the time of day,
Figure 640536DEST_PATH_IMAGE061
is shown in
Figure 211064DEST_PATH_IMAGE001
The output of the output layer at the time instant,
Figure 90027DEST_PATH_IMAGE062
is shown in
Figure 482831DEST_PATH_IMAGE001
The neural network prediction value of the echo state at the moment,
Figure 130850DEST_PATH_IMAGE063
is shown in
Figure 623011DEST_PATH_IMAGE001
At the moment, the first part inside the reserve tank
Figure 989270DEST_PATH_IMAGE064
The state of the individual neurons is known,
Figure 920186DEST_PATH_IMAGE065
is shown in
Figure 625974DEST_PATH_IMAGE001
At that moment, the state in the reserve tank. Initializing the state of a reserve pool
Figure 351353DEST_PATH_IMAGE066
，
Figure 408171DEST_PATH_IMAGE067
。
The internal state of the reserve pool neurons is updated as follows:
Figure 595308DEST_PATH_IMAGE068
the internal state of the output layer neurons is updated as follows:
Figure 952340DEST_PATH_IMAGE069
Figure 848620DEST_PATH_IMAGE070
which represents a non-linear activation function,
Figure 923893DEST_PATH_IMAGE071
which represents a linear activation function, is shown,
Figure 931032DEST_PATH_IMAGE072
indicating the amount of offset.
Preferably: the sparse linking rate is 0.01-0.05.
Preferably: the connection weight matrix
Figure 408149DEST_PATH_IMAGE051
Is less than 1.
Preferably:
Figure 475331DEST_PATH_IMAGE051
the step input of the second dynamic matrix prediction control system is wind power prediction error, and corresponding sampling data of wind power unit step response are obtained
Figure 303479DEST_PATH_IMAGE074
，
Figure 848730DEST_PATH_IMAGE075
，
Figure 180354DEST_PATH_IMAGE076
Modeling a time domain for a step response prediction model of a second dynamic matrix predictive control system.
Compared with the prior art, the invention has the following beneficial effects:
the method adopts more than two initial wind power prediction submodels, then adopts a first dynamic matrix prediction control system to correct the predicted values of the more than two initial wind power prediction submodels, and makes up the inaccuracy of the traditional wind power prediction method in wind power prediction calculation under extreme conditions through feedback correction and limited time domain rolling optimization. Secondly, dynamically determining the weight of the predicted value of the initial wind power prediction submodel through an echo state neural network, preventing uncertain data from influencing the predicted value, and well integrating the characteristics of each initial wind power prediction submodel. Finally, the second dynamic matrix prediction control system is adopted to correct the predicted value of the echo state neural network, the dynamic matrix prediction control is utilized again, based on feedback correction and finite time domain rolling optimization, the prediction failure of the echo state neural network is avoided, and by the means, the influence on the inaccuracy of wind power prediction under extreme conditions can be well relieved, the optimal prediction of future wind power of the wind power station is realized, and meanwhile, the wind power prediction precision is high.
Drawings
FIG. 1 is a schematic flow chart of an embodiment of the present invention.
Detailed Description
The present invention is further illustrated by the following description in conjunction with the accompanying drawings and the specific embodiments, it is to be understood that these examples are given solely for the purpose of illustration and are not intended as a definition of the limits of the invention, since various equivalent modifications will occur to those skilled in the art upon reading the present invention and fall within the limits of the appended claims.
A wind power prediction method based on dynamic matrix predictive control is disclosed, as shown in FIG. 1, and comprises the following steps:
step 1, collecting historical wind speed, topographic features, numerical weather forecast and historical wind power of a wind power plant as a historical data set, and dividing the historical data set into an initial wind power historical prediction data set and an echo state neural network historical prediction data set.
And 2, establishing more than two initial wind power prediction submodels according to the historical prediction data set of the initial wind power. Obtaining a prediction data set according to the history of the initial wind power
Figure 684017DEST_PATH_IMAGE001
At the first moment
Figure 757319DEST_PATH_IMAGE002
Wind power prediction value of initial wind power prediction submodel
Figure 106260DEST_PATH_IMAGE003
，
Figure 557970DEST_PATH_IMAGE077
，
Figure 701376DEST_PATH_IMAGE005
Representing the number of sub-models of the initial wind power predictor. The initial wind power prediction submodel includes a numerical weather wind power prediction model for simulating atmospheric dynamics based on a physical principle and boundary conditions, a wind power prediction model based on autoregressive moving average, a wind power prediction model based on kalman filtering, a wind power prediction model based on a markov chain, a wind power prediction model based on a gray theory, and a wind power prediction model based on artificial intelligence (neural network, support vector machine, limit learning machine, etc.), that is, the initial wind power prediction submodel in this embodiment may be an existing wind power prediction model.
Step 3, the product obtained in the step 2 is processed
Figure 238536DEST_PATH_IMAGE001
At the first moment
Figure 125590DEST_PATH_IMAGE002
Wind power prediction value of initial wind power prediction submodel
Figure 697385DEST_PATH_IMAGE003
And detected
Figure 277271DEST_PATH_IMAGE001
Actual wind power at time of day
Figure 239411DEST_PATH_IMAGE006
Comparing to obtain the prediction error of the sub model of the initial wind power predictor
Figure 930155DEST_PATH_IMAGE007
，
Figure 356457DEST_PATH_IMAGE008
。
Step 4, predicting errors of the initial wind power prediction submodels obtained in the step 4 through a first dynamic matrix prediction control system
Figure 107245DEST_PATH_IMAGE007
Feeding back the wind power predicted value of the initial wind power prediction submodel, and carrying out feedback correction on the wind power predicted value of the initial wind power prediction submodel to obtain a wind power prediction correction value of the initial wind power prediction submodel
Figure 353418DEST_PATH_IMAGE009
And further obtaining a wind power prediction correction matrix of the initial wind power prediction submodel
Figure 113433DEST_PATH_IMAGE078
，
Figure 394241DEST_PATH_IMAGE011
Indicating the prediction time.
The step input of the first dynamic matrix predictive control system is the prediction error of the initial wind power prediction submodel, and the sampling data of the corresponding wind power unit step response is obtained
Figure 519192DEST_PATH_IMAGE079
，
Figure 252662DEST_PATH_IMAGE080
，
Figure 550788DEST_PATH_IMAGE019
Modeling a time domain for a step response prediction model of a first dynamic matrix predictive control system, the step response prediction model of the first dynamic matrix predictive control system:
Figure 420524DEST_PATH_IMAGE020
wherein the content of the first and second substances,
Figure 513114DEST_PATH_IMAGE021
is at the same time
Figure 733879DEST_PATH_IMAGE022
Time of day prediction future
Figure 570117DEST_PATH_IMAGE023
At a moment in time have
Figure 559939DEST_PATH_IMAGE024
A continuous control increment
Figure 823430DEST_PATH_IMAGE025
，
Figure 531492DEST_PATH_IMAGE026
，… ，
Figure 437000DEST_PATH_IMAGE027
Outputting the wind power predicted value under the action,
Figure 281328DEST_PATH_IMAGE024
in order to control the length of the time domain,
Figure 715720DEST_PATH_IMAGE023
indicating a roll-optimized temporal length.
Figure 911078DEST_PATH_IMAGE028
Is at the same time
Figure 557960DEST_PATH_IMAGE022
Time of day prediction future
Figure 522374DEST_PATH_IMAGE023
Outputting the wind power predicted value under the action of no control increment at each moment,
Figure 862089DEST_PATH_IMAGE029
is a dynamic matrix whose elements are step response coefficients describing the dynamic behavior of the system,
Figure 364918DEST_PATH_IMAGE030
for the future from now on
Figure 81070DEST_PATH_IMAGE024
The control variable at each moment.
In that
Figure 899990DEST_PATH_IMAGE022
Wind power predicted at time without control increment
Figure 941764DEST_PATH_IMAGE025
Under the action of
Figure 846135DEST_PATH_IMAGE019
The output predicted value at each time is
Figure 365978DEST_PATH_IMAGE031
。
In that
Figure 304985DEST_PATH_IMAGE022
The wind power predicted at the moment has control increment
Figure 252081DEST_PATH_IMAGE025
Under the action of
Figure 643748DEST_PATH_IMAGE019
The output predicted value at each time is
Figure 967282DEST_PATH_IMAGE032
。
The feedback correction of the first dynamic matrix predictive control system is as follows: according to
Figure 760794DEST_PATH_IMAGE033
Constantly exerting control action on wind power value
Figure 878792DEST_PATH_IMAGE034
Then, at
Figure 757755DEST_PATH_IMAGE081
The actual output is collected at all times
Figure 619401DEST_PATH_IMAGE036
And
Figure 267420DEST_PATH_IMAGE033
output prediction value of step response prediction model based on first dynamic matrix prediction control system at moment
Figure 821898DEST_PATH_IMAGE082
Comparing to obtain real-time prediction error
Figure 922578DEST_PATH_IMAGE038
：
Figure 322335DEST_PATH_IMAGE039
By predicting the error in real time
Figure 356019DEST_PATH_IMAGE038
The weighting modifies the prediction of the future output, i.e.:
Figure 815819DEST_PATH_IMAGE040
in the formula (I), the compound is shown in the specification,
Figure 403796DEST_PATH_IMAGE041
is composed of
Figure 872823DEST_PATH_IMAGE042
Predicted future of time corrected by error
Figure 495434DEST_PATH_IMAGE019
The predicted wind power at each moment in time,
Figure 391715DEST_PATH_IMAGE043
is an error correction vector, i.e. a weight coefficient applied when error correcting the predicted values at different time instants, wherein,
Figure 466987DEST_PATH_IMAGE044
。
the optimized performance indexes of the first dynamic matrix predictive control system are as follows:
Figure 474126DEST_PATH_IMAGE083
wherein the content of the first and second substances,
Figure 951244DEST_PATH_IMAGE084
the performance index of the optimization is shown,
Figure 18426DEST_PATH_IMAGE047
a weighting coefficient representing a tracking error of the optical disc,
Figure 580994DEST_PATH_IMAGE048
show in the future
Figure 126245DEST_PATH_IMAGE049
The desired value of the output wind power is rolled in time,
Figure 723449DEST_PATH_IMAGE050
a weighting factor representing a control increment.
Step 5, constructing an echo state neural network, wherein the echo state neural network comprises an input layer, a reserve pool and an output layer, and the method comprises the following steps:
an input layer: comprises that
Figure 961532DEST_PATH_IMAGE024
And an input neuron.
A reserve pool: is composed of
Figure 769255DEST_PATH_IMAGE019
A sparse network formed by connecting a plurality of neurons,for connection weight matrix between neurons in reserve pool
Figure 383776DEST_PATH_IMAGE051
It is shown that,
Figure 569906DEST_PATH_IMAGE052
representing neurons within the reservoir
Figure 978891DEST_PATH_IMAGE053
And neurons
Figure 516052DEST_PATH_IMAGE054
Connection weight, connection weight matrix
Figure 668684DEST_PATH_IMAGE051
Is a sparse matrix, the sparsity of the connection between neurons in the reserve pool is expressed by a sparse connection rate, which is generally selected to be 0.01-0.05, and in order to make the network have the property of echo state to ensure the stability of the network, a connection weight matrix
Figure 709321DEST_PATH_IMAGE051
Must be smaller than 1. Matrix for connection weights between neurons in input layer and neurons in dynamic reservoir
Figure 23628DEST_PATH_IMAGE055
And (4) showing. The pool accepts inputs in two directions, one from the input layer and the other from the state of the previous output layer.
An output layer: comprises 1 output neuron, a matrix for connecting weights from neuron in reserve pool to neuron in output layer
Figure 313664DEST_PATH_IMAGE012
Representing a matrix of connection weights from neurons within the output layer to neurons within the reservoir
Figure 4408DEST_PATH_IMAGE056
And (4) showing.
The echo state neural network is
Figure 430710DEST_PATH_IMAGE001
The values at the time are expressed as follows:
Figure 915918DEST_PATH_IMAGE057
Figure 427671DEST_PATH_IMAGE058
Figure 922107DEST_PATH_IMAGE059
Figure 671757DEST_PATH_IMAGE060
is shown in
Figure 593445DEST_PATH_IMAGE001
The input layer input is made at the time of day,
Figure 592494DEST_PATH_IMAGE061
is shown in
Figure 890620DEST_PATH_IMAGE001
The output of the output layer at the time instant,
Figure 760356DEST_PATH_IMAGE062
is shown in
Figure 852946DEST_PATH_IMAGE001
The neural network prediction value of the echo state at the moment,
Figure 73712DEST_PATH_IMAGE063
is shown in
Figure 909950DEST_PATH_IMAGE001
At the moment, the first part inside the reserve tank
Figure 899771DEST_PATH_IMAGE064
The state of the individual neurons is known,
Figure 163262DEST_PATH_IMAGE065
is shown in
Figure 871324DEST_PATH_IMAGE001
At that moment, the state in the reserve tank. Initializing the status of a reserve pool
Figure 511253DEST_PATH_IMAGE066
，
Figure 621160DEST_PATH_IMAGE067
。
The internal state of the reserve pool neurons is updated as follows:
Figure 789973DEST_PATH_IMAGE068
the internal state of the output layer neurons is updated as follows:
Figure 250911DEST_PATH_IMAGE069
Figure 428951DEST_PATH_IMAGE070
which is representative of a non-linear activation function,
Figure 127786DEST_PATH_IMAGE071
which represents a linear activation function, is shown,
Figure 733079DEST_PATH_IMAGE072
indicating the amount of offset.
During training, inputting the wind power prediction correction matrix of the initial wind power prediction submodel obtained in the step 4 into the neural network in the echo state, and inputting the correction matrix into the neural network in the echo stateTraining the channels, wherein a least square method is adopted to estimate a connection weight matrix from the neuron in the reserve pool to the neuron in the output layer
Figure 173592DEST_PATH_IMAGE012
And obtaining the trained echo state neural network.
And 6, processing the historical prediction data set of the neural network in the echo state through the steps 2 to 4 to obtain a correction set of the neural network in the echo state. Inputting the obtained echo state neural network correction set into the trained echo state neural network to obtain the predicted value of the echo state neural network
Figure 155323DEST_PATH_IMAGE013
。
Step 7, the echo state neural network predicted value obtained in the step 6 is used
Figure 974243DEST_PATH_IMAGE013
And detected
Figure 750438DEST_PATH_IMAGE001
Actual wind power at time of day
Figure 920389DEST_PATH_IMAGE006
Comparing to obtain wind power prediction error
Figure 440232DEST_PATH_IMAGE014
，
Figure 113658DEST_PATH_IMAGE015
。
Step 8, the wind power prediction error obtained in the step 7 is subjected to prediction by a second dynamic matrix prediction control system
Figure 326334DEST_PATH_IMAGE014
Is fed back to
Figure 983580DEST_PATH_IMAGE001
Neural network prediction of echo state at time, pair
Figure 510376DEST_PATH_IMAGE001
The wind power predicted value of the echo state neural network predicted value at the moment is subjected to feedback correction to obtain the wind power predicted value after the second correction
Figure 835047DEST_PATH_IMAGE001
Temporal echo state neural network prediction correction
Figure 218624DEST_PATH_IMAGE016
。
The step input of the second dynamic matrix prediction control system is wind power prediction error, and corresponding sampling data of wind power unit step response are obtained
Figure 97587DEST_PATH_IMAGE074
，
Figure 959233DEST_PATH_IMAGE075
，
Figure 168104DEST_PATH_IMAGE076
And modeling a time domain for the step response prediction model of the second dynamic matrix prediction control system, wherein other step response prediction models, feedback correction and optimization performance indexes are similar to those of the first dynamic matrix prediction control system and are not repeated herein.
The embodiment carries out dynamic matrix control prediction for the submodels once through twice dynamic matrix control prediction, carries out dynamic matrix control prediction for the echo state neural network once, and simultaneously adopts the echo state neural network to carry out dynamic weight determination for each submodel, therefore, the influence of extreme conditions on wind power can be effectively prevented, the inherent volatility and intermittent defects of wind power are reduced simultaneously, the difficulty of making a power generation plan caused by large-scale access of the wind power is reduced, the challenge brought by the wind power to the dispatching operation of a power system is reduced, the peak regulation and the frequency modulation pressure of the power system are relieved, and the wind power receiving capacity is improved. In addition, wind power plant development enterprises can also reasonably arrange the maintenance of the wind turbine equipment by selecting weather with smaller wind power by using wind power forecast, and the power generation loss caused by the fact that the wind turbine cannot generate power due to maintenance is reduced as much as possible.
The above description is only of the preferred embodiments of the present invention, and it should be noted that: it will be apparent to those skilled in the art that various modifications and adaptations can be made without departing from the principles of the invention and these are intended to be within the scope of the invention.

Claims (8)

1. A wind power prediction method based on dynamic matrix predictive control is characterized by comprising the following steps:
step 1, collecting historical wind speed, topographic features, numerical weather forecast and historical wind power of a wind power plant as a historical data set, and dividing the historical data set into an initial wind power historical prediction data set and an echo state neural network historical prediction data set;
step 2, establishing more than two initial wind power prediction submodels according to the historical prediction data set of the initial wind power; obtaining a prediction data set according to the history of the initial wind power
Figure 968762DEST_PATH_IMAGE001
At the first moment
Figure 10536DEST_PATH_IMAGE002
Wind power prediction value of initial wind power prediction submodel
Figure 180486DEST_PATH_IMAGE003
，
Figure 700329DEST_PATH_IMAGE004
，
Figure 842597DEST_PATH_IMAGE005
Representing the number of the initial wind power prediction submodels;
step 3, the product obtained in the step 2
Figure 507803DEST_PATH_IMAGE001
At the first moment
Figure 899470DEST_PATH_IMAGE002
Wind power prediction value of initial wind power prediction submodel
Figure 488583DEST_PATH_IMAGE003
And detected
Figure 282096DEST_PATH_IMAGE001
Actual wind power at time of day
Figure 400093DEST_PATH_IMAGE006
Comparing to obtain the prediction error of the sub-model of the initial wind power predictor
Figure 279056DEST_PATH_IMAGE007
，
Figure 343964DEST_PATH_IMAGE008
；
Step 4, predicting errors of the initial wind power prediction submodels obtained in the step 4 through a first dynamic matrix prediction control system
Figure 257563DEST_PATH_IMAGE007
Feeding back the wind power predicted value of the initial wind power prediction submodel, and carrying out feedback correction on the wind power predicted value of the initial wind power prediction submodel to obtain a wind power prediction correction value of the initial wind power prediction submodel
Figure 15303DEST_PATH_IMAGE009
And further obtaining a wind power prediction correction matrix of the initial wind power prediction submodel
Figure 201737DEST_PATH_IMAGE010
，
Figure 601495DEST_PATH_IMAGE011
Representing a predicted time;
step 5, constructing an echo state neural network, wherein the echo state neural network comprises an input layer, a reserve pool and an output layer which are connected in sequence;
during training, inputting the wind power prediction correction matrix of the initial wind power prediction submodel obtained in the step 4 into an echo state neural network, and training the echo state neural network, wherein a least square method is adopted to estimate a connection weight matrix from the neuron in the reserve pool to the neuron in the output layer
Figure 369600DEST_PATH_IMAGE012
Obtaining a trained echo state neural network;
step 6, processing the historical prediction data set of the echo state neural network through the steps 2 to 4 to obtain an echo state neural network correction set; inputting the obtained echo state neural network correction set into the trained echo state neural network to obtain the predicted value of the echo state neural network
Figure 829400DEST_PATH_IMAGE013
；
Step 7, the echo state neural network predicted value obtained in the step 6 is used
Figure 417376DEST_PATH_IMAGE013
And detected
Figure 620824DEST_PATH_IMAGE001
Actual wind power at time of day
Figure 243435DEST_PATH_IMAGE006
Comparing to obtain wind power prediction error
Figure 139716DEST_PATH_IMAGE014
，
Figure 214988DEST_PATH_IMAGE015
；
Step 8, the wind power prediction error obtained in the step 7 is subjected to prediction by a second dynamic matrix prediction control system
Figure 222127DEST_PATH_IMAGE014
Is fed back to
Figure 699245DEST_PATH_IMAGE001
Neural network prediction of echo state at time, pair
Figure 766427DEST_PATH_IMAGE001
The wind power predicted value of the echo state neural network predicted value at the moment is subjected to feedback correction to obtain the wind power predicted value after the second correction
Figure 328996DEST_PATH_IMAGE001
Temporal echo state neural network prediction correction
Figure 874246DEST_PATH_IMAGE016
。
2. The wind power prediction method based on dynamic matrix predictive control according to claim 1, characterized in that: the step input of the first dynamic matrix predictive control system is the prediction error of the initial wind power predictor sub-model, and the sampling data of the corresponding wind power unit step response is obtained
Figure 471450DEST_PATH_IMAGE017
，
Figure 709533DEST_PATH_IMAGE018
，
Figure 556135DEST_PATH_IMAGE019
Modeling the time domain for a step response prediction model of a first dynamic matrix predictive control system, firstStep response prediction model of dynamic matrix predictive control system:
Figure 905077DEST_PATH_IMAGE020
wherein the content of the first and second substances,
Figure 91208DEST_PATH_IMAGE021
is at least
Figure 500192DEST_PATH_IMAGE022
Time of day prediction future
Figure 37353DEST_PATH_IMAGE023
At a moment in time have
Figure 189986DEST_PATH_IMAGE024
A continuous control increment
Figure 496202DEST_PATH_IMAGE025
，
Figure 544929DEST_PATH_IMAGE026
，… ，
Figure 569386DEST_PATH_IMAGE027
Outputting the wind power predicted value under the action,
Figure 525710DEST_PATH_IMAGE024
to control the time domain length;
Figure 686433DEST_PATH_IMAGE028
is at the same time
Figure 171641DEST_PATH_IMAGE022
Time of day prediction future
Figure 683393DEST_PATH_IMAGE023
Outputting the wind power predicted value under the action of no control increment at each moment,
Figure 177829DEST_PATH_IMAGE029
is a dynamic matrix whose elements are step response coefficients describing the dynamic behavior of the system,
Figure 193058DEST_PATH_IMAGE030
for the future from now on
Figure 114746DEST_PATH_IMAGE024
A control variable at each time;
in that
Figure 871654DEST_PATH_IMAGE022
Wind power predicted at time without control increment
Figure 169780DEST_PATH_IMAGE025
Under the action of
Figure 305095DEST_PATH_IMAGE019
The output predicted value at each time is
Figure 945155DEST_PATH_IMAGE031
；
In that
Figure 165921DEST_PATH_IMAGE022
The wind power predicted at the moment has control increment
Figure 2158DEST_PATH_IMAGE025
Under the action of
Figure 991980DEST_PATH_IMAGE019
The output predicted value at each time is
Figure 989892DEST_PATH_IMAGE032
。
3. The wind power prediction method based on dynamic matrix predictive control according to claim 2, characterized in that: the feedback correction of the first dynamic matrix predictive control system is as follows: according to
Figure 697954DEST_PATH_IMAGE033
Constantly exerting control action on wind power value
Figure 72303DEST_PATH_IMAGE034
Then, at
Figure 182211DEST_PATH_IMAGE035
The actual output is collected at all times
Figure 616603DEST_PATH_IMAGE036
And
Figure 608699DEST_PATH_IMAGE033
output prediction value of step response prediction model based on first dynamic matrix prediction control system at moment
Figure 786739DEST_PATH_IMAGE037
Comparing to obtain real-time prediction error
Figure 751153DEST_PATH_IMAGE038
：
Figure 559709DEST_PATH_IMAGE039
By predicting the error in real time
Figure 976784DEST_PATH_IMAGE038
The weighting modifies the prediction of future outputs, i.e.:
Figure 755253DEST_PATH_IMAGE040
in the formula (I), the compound is shown in the specification,
Figure 574173DEST_PATH_IMAGE041
is composed of
Figure 350368DEST_PATH_IMAGE042
Predicted future of time error corrected
Figure 520318DEST_PATH_IMAGE019
The predicted wind power at each moment in time,
Figure DEST_PATH_IMAGE043
is an error correction vector, i.e. a weight coefficient applied when error correcting the predicted values at different time instants, wherein,
Figure 164795DEST_PATH_IMAGE044
。
4. the wind power prediction method based on dynamic matrix predictive control according to claim 3, characterized in that: the optimized performance indexes of the first dynamic matrix predictive control system are as follows:
Figure 838222DEST_PATH_IMAGE045
wherein the content of the first and second substances,
Figure 50897DEST_PATH_IMAGE046
the performance index of the optimization is shown,
Figure 442565DEST_PATH_IMAGE047
a weighting coefficient representing a tracking error of the optical disc,
Figure 766098DEST_PATH_IMAGE048
show in the future
Figure 294032DEST_PATH_IMAGE049
The desired value of the output wind power is rolled in time,
Figure 880871DEST_PATH_IMAGE050
a weighting factor representing a control increment.
5. The wind power prediction method based on dynamic matrix predictive control according to claim 4, characterized in that: the input layer: comprises that
Figure 556572DEST_PATH_IMAGE024
An input neuron;
a reserve pool: is composed of
Figure 418217DEST_PATH_IMAGE019
Sparse network formed by connecting individual neurons, and connection weight matrix between neurons in reserve pool
Figure 66236DEST_PATH_IMAGE051
，
Figure 34365DEST_PATH_IMAGE052
Representing neurons within the reservoir
Figure 135045DEST_PATH_IMAGE053
And neurons
Figure 65961DEST_PATH_IMAGE054
Connection weight, connection weight matrix
Figure 834066DEST_PATH_IMAGE051
Is a sparse matrix, the sparsity of connection between neurons in the reservoir is expressed by sparse connection rate, and the input layerMatrix for connection weights between internal neurons and internal neurons in dynamic reservoir
Figure 293866DEST_PATH_IMAGE055
Represents; the reserve pool accepts inputs in two directions, one from the input layer and the other from the output of the state of the previous output layer;
an output layer: comprises 1 output neuron, a matrix for connecting weights from neuron in reserve pool to neuron in output layer
Figure 881842DEST_PATH_IMAGE012
Representing a matrix of connection weights from neurons within the output layer to neurons within the reservoir
Figure 85290DEST_PATH_IMAGE056
Represents;
the echo state neural network is in
Figure 707901DEST_PATH_IMAGE001
The values at the time are expressed as follows:
Figure 338603DEST_PATH_IMAGE057
Figure 679454DEST_PATH_IMAGE058
Figure 686594DEST_PATH_IMAGE059
wherein the content of the first and second substances,
Figure 163711DEST_PATH_IMAGE060
is shown in
Figure 27631DEST_PATH_IMAGE001
The input layer input is made at the time of day,
Figure 918095DEST_PATH_IMAGE061
is shown in
Figure 728925DEST_PATH_IMAGE001
The output of the output layer at the time instant,
Figure 326129DEST_PATH_IMAGE062
is shown in
Figure 33054DEST_PATH_IMAGE001
The neural network prediction value of the echo state at the moment,
Figure 82918DEST_PATH_IMAGE063
is shown in
Figure 431860DEST_PATH_IMAGE001
At the moment, the first part inside the reserve tank
Figure 883570DEST_PATH_IMAGE064
The state of the individual neurons is known,
Figure 89292DEST_PATH_IMAGE065
is shown in
Figure 626453DEST_PATH_IMAGE001
The state in the reserve tank at the moment; initializing the status of a reserve pool
Figure 513506DEST_PATH_IMAGE066
，
Figure 819723DEST_PATH_IMAGE067
；
The internal state of the reserve pool neurons is updated as follows:
Figure 399608DEST_PATH_IMAGE068
the internal state of the output layer neurons is updated as follows:
Figure 424065DEST_PATH_IMAGE069
Figure 114810DEST_PATH_IMAGE070
which represents a non-linear activation function,
Figure 9953DEST_PATH_IMAGE071
which represents a linear activation function, is shown,
Figure 760740DEST_PATH_IMAGE072
indicating the amount of offset.
6. The wind power prediction method based on dynamic matrix predictive control according to claim 5, characterized in that: the sparse linking rate is 0.01-0.05.
7. The wind power prediction method based on dynamic matrix predictive control according to claim 6, characterized in that: the connection weight matrix
Figure 272493DEST_PATH_IMAGE051
Is less than 1.
8. The wind power prediction method based on dynamic matrix predictive control according to claim 7, characterized in that: the step input of the second dynamic matrix predictive control system is a wind power prediction error, and sampling data of corresponding wind power unit step response are obtained
Figure 501349DEST_PATH_IMAGE074
，
Figure 212120DEST_PATH_IMAGE075
，
Figure 117497DEST_PATH_IMAGE076
Modeling a time domain for a step response prediction model of a second dynamic matrix predictive control system.
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