CN108985313A - AGC system discrimination method based on big data and Bayesian neural network - Google Patents
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Abstract
The present invention relates to a kind of AGC system discrimination method based on big data and Bayesian neural network, comprising: 1, acquire the historical data of AGC system, pretreatment obtains sample data;2, Bayesian neural network is initialized;3, the input and output of hidden layer, each neuron of output layer are calculated, calculate the difference of reality output and Bayesian neural network output, and according to MSE criterion calculation error;4, whether error in judgement reaches requirement, if so, carry out step 6, otherwise, between output layer and hidden layer weight and threshold be modified, between input layer and hidden layer weight and threshold be modified, update each connection weight, study number adds 1;5, step 3~4 are repeated, until reaching error requirements or maximum study number;6, Bayesian neural network, the mathematical model recognized are calculated.Compared with classic BP neural network identification method, the present invention possesses better identification precision and faster convergence rate.
Description
Technical field
The present invention relates to information control technology field, more particularly, to a kind of based on big data and Bayesian neural network
AGC system discrimination method.
Background technique
End the end of the year in 2017, China's capacity of installed generator is 17.7 hundred million kilowatts, and thermoelectricity installed capacity accounts for total installation of generating capacity
62.2%, thermal power generation is still the main forms of electricity generation in China, however as economy enter new normality, face resource and ring
The double constraints in border, the situation that Thermal Power Generation Industry faces are more and more severeer.AGC is one of dispatching of power netwoks advanced technology means,
Its main task is to realize power network schedule automation Energy Management System (Energy Management System, abbreviation EMS)
With the closed-loop control between generator group coordination and control system (Coordination Control System, abbreviation CCS).It is extra-high
The fast development of voltage electric grid, AC and DC transmission technology and new energy more improves the complexity of dispatching of power netwoks and control, and
The fired power generating unit for having depth peak modulation capacity is therefore, to establish optimization AGC system model using energy consumption sacrifice as cost, promoted
AGC system Control platform is the most important thing of work at present.
In recent years, intelligence manufacture temperature is surging, and big data is the hot spot direction of each area research, target first is that according to
Data analysis result adjust automatically control strategy and way to manage keep power plant's production long-term to guarantee that fired power generating unit operates normally
In safety, economical and environmentally friendly in operating status.With the breakthrough development of deep neural network technology, artificial intelligence is in the world
The situation of persistently overheating is presented in range, 2017, " artificial intelligence " was put into the government work report in two Conferences for the first time, this is not
Great attention of the China to Artificial Intelligence Development is illustrated only, more indicates that " artificial intelligence " formally enters " Chinese rhythm ".Closely
Several years, big data caused the highest attention of all trades and professions, relied on the artificial intelligence of big data that can more play unlimited potentiality, can
To say that big data opens a new epoch.
Artificial intelligent recognition method is research emphasis in recent years, wherein such as neural network, genetic algorithm, particle group optimizing
Algorithm scheduling algorithm all again system be in achieve great research achievement, but there is also some shortcomings.It is directed to although having at present
AGC system carries out the research of the intelligent algorithms such as neural network modeling, but how sufficiently to combine power plant's big data and retrograde intelligence again
Can identification aspect still without providing practical guidance.Therefore by acquisition power plant's correlation operation data, power plant's big data background is studied
The lower feasibility that nerve root network modeling method is applied to AGC system identification, to the control performance for further increasing AGC system
Also significant.
Summary of the invention
It is an object of the present invention to overcome the above-mentioned drawbacks of the prior art and provide one kind based on big data with
The AGC system discrimination method of Bayesian neural network.
The purpose of the present invention can be achieved through the following technical solutions:
A kind of AGC system discrimination method based on big data and Bayesian neural network, comprising the following steps:
S1, the historical data for acquiring AGC system, obtain sample data after pre-processing to historical data;
S2, Bayesian neural network is initialized;
S3, calculate hidden layer, output layer each neuron is output and input, calculate reality output and Bayesian neural network
The difference of output, and according to MSE criterion calculation error;
Whether S4, error in judgement reach requirement, if reaching requirement, carry out step S6, otherwise, to output layer and imply
Layer between weight and threshold be modified, between input layer and hidden layer weight and threshold be modified, update each company
Weight is connect, study number adds 1;
S5, step S3~S4 is repeated, until reaching error requirements or maximum study number;
S6, the final result for calculating Bayesian neural network hidden layer and output layer, the mathematical model recognized.
Preferably, the step S1 is specifically included:
S11, the historical data for acquiring AGC system, using the noise in smoothing method removal historical data;
S12, zero initialization and normalized are carried out to historical data;
S13, it selects to obtain sample data from historical data using nearest neighbour method.
Preferably, smoothing method uses LOESS smoothing method in the step S11, and this method specifically includes:
S111, with arbitrary number strong point x in historical dataiCentered on determine a section, interval width depend on participation office
The observed value number that portion returns;
S112, the weight for defining all the points in the section;
S113, the point in the section is fitted to conic section, obtains match value yi;
S114, the processing that step S111~S113 is carried out to point each in historical data, obtain one group of smooth point, xiIt is flat
Sliding point is exactly xiFitting the match point (x on the straight line comei,yi), all the points are connected with short straight line, obtain LOESS recurrence
Curve obtains LOESS sharpening result according to LOESS regression curve.
Preferably, the weight in the step S112 is by cube weighting function T (u) definition:
Wherein, u=Δi(x)/Δ(q)(x), Δi(x)=| xi- x | indicate the distance of point x to xi, xiIndicate historical data
Arbitrary number strong point, x indicate historical data in remove xiExcept other data points, Δ(q)(x) Δ is indicatedi(x) maximum value.
Preferably, include: according to the process that LOESS regression curve obtains LOESS sharpening result in the step S114
The quadratic sum for defining the difference that error function is match value and actual value, returns LOESS using gradient descent method
Curve approaches y=θixi, y expression inputs or output data smoothed curve, θiIndicate xiThe slope for locating smoothed curve, to obtain
LOESS sharpening result.
Preferably, zero initialization process specifically includes in the step S12:
Wherein, u* (k), y* (k), u (k), y (k) respectively indicate the value after input data zero initializes, at the beginning of output data zero
The value before value, the initialization of input data zero, the value after the initialization of output data zero after beginningization, N is constant, indicates access evidence
Initial value of the mean value of top N as data.
Compared with prior art, the present invention uses bayesian algorithm, converts statistical concept for uncertainty, will weigh
Value optimization problem is converted into solution normal law error minimum problems and improves algorithm to avoid the case where falling into local optimum
Rapidity and accuracy, compared with classic BP neural network identification method, possess better identification precision and faster receive
Speed is held back, is had important practical significance to the automatic control level for improving fired power generating unit.
Detailed description of the invention
Fig. 1 is that the present invention is based on the AGC system discrimination method flow diagrams of big data and Bayesian neural network;
Fig. 2 is certain the 1000MW extra-supercritical unit AGC system lofty tone gate valve position input curve figure acquired in embodiment;
Fig. 3 is certain the 1000MW extra-supercritical unit AGC system feedwater flow input curve figure acquired in embodiment;
Fig. 4 is certain the total fuel quantity input curve figure of 1000MW extra-supercritical unit AGC system acquired in embodiment;
Fig. 5 is certain the 1000MW extra-supercritical unit AGC system actual power output curve diagram acquired in embodiment;
Fig. 6 is certain the 1000MW extra-supercritical unit AGC system centrum's temperature output curve diagram acquired in embodiment;
Fig. 7 is certain the 1000MW extra-supercritical unit AGC system main steam pressure output curve diagram acquired in embodiment;
Fig. 8 is the lofty tone gate valve position sample data curve graph obtained in embodiment using the method for the present invention;
Fig. 9 is the feedwater flow sample data curve graph obtained in embodiment using the method for the present invention;
Figure 10 is the total fuel quantity sample data curve graph obtained in embodiment using the method for the present invention;
Figure 11 is the actual power sample data curve graph obtained in embodiment using the method for the present invention;
Figure 12 is the centrum's temperature sample data curve graph obtained in embodiment using the method for the present invention;
Figure 13 is the main steam pressure sample data curve graph obtained in embodiment using the method for the present invention;
Figure 14 is the training of actual power when being recognized using the method for the present invention and classical BP algorithm to AGC system in embodiment
Comparative result figure;
Figure 15 is the instruction of centrum's temperature when being recognized using the method for the present invention and classical BP algorithm to AGC system in embodiment
Practice comparative result figure;
Figure 16 is the instruction of main steam pressure when being recognized using the method for the present invention and classical BP algorithm to AGC system in embodiment
Practice comparative result figure;
Figure 17 is practical function obtained by the method for the present invention and classical BP algorithm when being verified in embodiment with remaining data
Rate test result comparison diagram;
Figure 18 is intermediate point obtained by the method for the present invention and classical BP algorithm when being verified in embodiment with remaining data
Temperature test result comparison diagram;
Figure 19 is main steam obtained by the method for the present invention and classical BP algorithm when being verified in embodiment with remaining data
Pressure testing results comparison diagram.
Specific embodiment
The present invention is described in detail with specific embodiment below in conjunction with the accompanying drawings.The present embodiment is with technical solution of the present invention
Premised on implemented, the detailed implementation method and specific operation process are given, but protection scope of the present invention is not limited to
Following embodiments.
A kind of AGC system discrimination method based on big data and Bayesian neural network, as shown in Figure 1, passing through acquisition AGC
The history big data of system selects lofty tone gate valve position, feedwater flow and total fuel quantity as mode input amount, actual power, in
Between put temperature and main steam pressure as model output, first using data smoothing, removal trend term, normalize and etc. into
Then line number Data preprocess is selected the sample data set that can characterize system performance using nearest neighbour method, utilizes Bayesian neural network
Model is trained the relationship between rear mining data, and establishes the AGC model based on Bayesian neural network, specifically include with
Lower step:
S1, the historical data for acquiring AGC system, obtain sample data after pre-processing to historical data, specifically include:
S11, the historical data for acquiring AGC system, using the noise in smoothing method removal historical data, the present embodiment
In, LOESS smoothing method is specifically used, smoothing method does not depend on the overall distribution and its parameter of data, and application is more flexible, tool
Body includes:
S111, with arbitrary number strong point x in historical dataiCentered on determine a section, width depend on q=f × n,
Wherein, q is the observed value number for participating in local regression, and f is that the number for the observed value for participating in local regression accounts for observed value number
Ratio, n is observed value number, in practical application, first selecting f value, the value of q is determined further according to f, n, increases f, q, smooth journey
Degree increases, otherwise reduces;
The weight of all the points in S112, interval of definition is defined by cube weighting function T (u):
Wherein, u=Δi(x)/Δ(q)(x), Δ is enabledi(x)=| xi- x | indicate point x to xiDistance, xiIndicate historical data
Arbitrary number strong point, x indicate historical data in remove xiExcept other data points, by Δi(x) it is arranged as Δ from small to large(i)
(x), Δ(q)(x) Δ is indicatedi(x) maximum value;
S113, the point in section is fitted to conic section, influence of the bigger point of weight to result is bigger, outside section
Point weight is 0, obtains match value yi;
S114, the processing that step S111~S113 is carried out to point each in historical data, obtain one group of smooth point, xiIt is flat
Sliding point is exactly xiFitting the match point (x on the straight line comei,yi), all the points are connected with short straight line, obtain LOESS recurrence
Curve defines the quadratic sum for the difference that error function is match value and actual value, makes LOESS regression curve using gradient descent method
Approach y=θixi, y expression inputs or output data smoothed curve, θiIndicate xiThe slope for locating smoothed curve, to obtain LOESS
Sharpening result.
S12, zero initialization and normalized, zero initialization process formula are carried out to historical data are as follows:
Wherein, u*(k)、y*(k), u (k), y (k) respectively indicate the value after input data zero initializes, at the beginning of output data zero
The value before value, the initialization of input data zero, the value after the initialization of output data zero after beginningization, N is constant, indicates access evidence
Initial value of the mean value of top N as data;
To the formula of data normalized are as follows:
Wherein, xminMinimum value before indicating to normalize in data, xmaxMaximum value before indicating to normalize in data,
xgiI-th of data after indicating normalization, xsIndicate the individual data point to be normalized;
S13, it selects to obtain sample data from historical data using nearest neighbour method, comprising:
S131, in data according to same intervals select 200 data groups be stored as initial training sample set X, due to number
It is big according to amount, acquisition time is long, it is believed that be obvious different between data, be not neighbour;
S132, when inputting new test sample xniWhen, calculate the Europe between the test sample and each initial training sample
Formula distance Ed:
In formula, XiIndicate arbitrary initial training sample value;
S133, judge test sample xniWith XiEuclidean distance whether be smaller value α, α is obtained by many experiments
The constant value that identification effect can be made best.If so, illustrating X in sample setiExisting xniNeighbour's value, then xniIt does not need to be added
Training sample set Xi;Conversely, then illustrating that the sample can embody different system features, need xniIt is selected into XiTo improve sample
Collection;
S134 repeats S132~S133, obtains final sample collection after traversing total data.
S2, Bayesian neural network is initialized, gives each connection weight at random, set error function, given and calculate essence
Degree, learning rate, maximum study number, and initialize study number.
S3, calculate hidden layer, output layer each neuron is output and input, calculate reality output and Bayesian neural network
The difference of output, and according to MSE criterion calculation error E.
Whether S4, error in judgement reach requirement, if reaching requirement, carry out step S6, otherwise, to output layer and imply
Layer between weight and threshold be modified, between input layer and hidden layer weight and threshold be modified, update each company
Weight is connect, study number adds 1.
S5, step S3~S4 is repeated, until reaching error requirements or maximum study number.
S6, the final result for calculating Bayesian neural network hidden layer and output layer save network, the mathematics recognized
Model.
After training obtains the Bayesian neural network of AGC system through the above steps, with remaining sampled data to being established
Network is tested.
Classical BP neural network setting in initial weight, initial learning rate value, the hidden layer number of plies, hidden layer unit number etc.
It sets and is all instructed without accurate theoretical foundation, exist and easily fall into the drawbacks such as local optimum, pace of learning be slow.The application uses pattra leaves
This algorithm improvement neural network carries out AGC system identification.The final purpose of neural metwork training is to find suitable weight vector ω,
So that root-mean-square error E obtains minimum value.Bayesian algorithm is to convert uncertainty to statistical concept, by weight optimizing
Problem is converted into solution normal law error minimum problems and improves the quick of algorithm to avoid the case where falling into local optimum
Property and accuracy.According to bayesian principle, probabilistic model is converted by traditional problem analysis, weight matrix ω will be asked to make posteriority
Distribution probability obtains max problem and is converted into normal law error functional minimum value problem of asking, that is, utilizes bayesian theory by weight
Optimization problem converts to solve minimum problems.
Embodiment
To verify the AGC system discrimination method identification precision with higher and faster convergence speed that the application is proposed
Degree, acquire certain 1000MW extra-supercritical unit history data, select lofty tone gate valve position, feedwater flow and total fuel quantity as
Mode input amount, actual power, centrum's temperature and main steam pressure acquire 16381 groups of data, adopt as model output
0.01s is divided between sample, then the input observed and curve of output are respectively as shown in Fig. 2~7.
The pretreatment such as zero initialization, data de-noising, normalization is carried out to all data according to the application mentioned discrimination method
Afterwards, 1000 groups of data are chosen as sample data using nearest neighbour method, the sample data curve of selection is as depicted in figures 8-13.By sample
Sampled data except notebook data is as test data, respectively using classical BP algorithm and the improved nerve net of the application Bayes
Network algorithm is trained and tests, and network parameter is arranged by 1 data of table when training:
1 neural network algorithm parameter setting of table
Hidden nodes | Maximum frequency of training | Learning rate | Minimum error values |
30 | 3000 | 0.00006 | 0.65*10^-35 |
After 3000 study, BP neural network algorithm and Bayesian neural network algorithm have been restrained, training result
With sample curve of output respectively as shown in Figure 14, Figure 15, Figure 16.It will be evident that classical BP neural network is changing from figure
Trend can train preferable as a result, still not in dramatic decrease or ascent stage and sample data acute variation when smaller
The identification effect of Bayesian neural network can be reached, although the worst error value of Bayesian neural network is larger, from entirety
From the point of view of still better than classical BP neural network.After model training is good, the result that remainder data is verified is respectively such as Figure 17, figure
18, shown in Figure 19.
From Picture study it is found that two methods test effect have it is significantly different.Actual power and main steam pressure force curve
Fluctuation compared with centrum's temperature curve is significantly smaller, thus in the prediction of the two output valves two methods show compared with
Good, although wherein the identification trend of classic BP is in the main true, but accuracy is not as good as Bayesian neural network.And in intermediate point temperature
It spends in the prediction of output valve, it is exactly the opposite, it is particularly evident in fluctuation larger part.
The learning error of Bayesian neural network drops to 153 from 2780 rapidly in preceding 1000 generation error, and effect is obvious, with
It is slowly varying up to training terminates near optimal value afterwards.Final error is 7.4576.Under identical network structure, from instruction
Practice result apparently, Bayesian neural network clearly compared with classical BP neural network advantage, test phase error still
It is smaller, and Bayesian neural network convergence rate is very fast.
Claims (6)
1. a kind of AGC system discrimination method based on big data and Bayesian neural network, which is characterized in that including following step
It is rapid:
S1, the historical data for acquiring AGC system, obtain sample data after pre-processing to historical data;
S2, Bayesian neural network is initialized;
S3, calculate hidden layer, output layer each neuron is output and input, calculate reality output and Bayesian neural network export
Difference, and according to MSE criterion calculation error;
Whether S4, error in judgement reach requirement, if reaching requirement, carry out step S6, otherwise, to output layer and hidden layer it
Between weight and threshold be modified, between input layer and hidden layer weight and threshold be modified, update each connection weight
Value, study number add 1;
S5, step S3~S4 is repeated, until reaching error requirements or maximum study number;
S6, the final result for calculating Bayesian neural network hidden layer and output layer, the mathematical model recognized.
2. the AGC system discrimination method according to claim 1 based on big data and Bayesian neural network, feature exist
In the step S1 is specifically included:
S11, the historical data for acquiring AGC system, using the noise in smoothing method removal historical data;
S12, zero initialization and normalized are carried out to historical data;
S13, it selects to obtain sample data from historical data using nearest neighbour method.
3. the AGC system discrimination method according to claim 2 based on big data and Bayesian neural network, feature exist
In smoothing method uses LOESS smoothing method in the step S11, and this method specifically includes:
S111, with arbitrary number strong point x in historical dataiCentered on determine a section, interval width depend on participate in local regression
Observed value number;
S112, the weight for defining all the points in the section;
S113, the point in the section is fitted to conic section, obtains match value yi;
S114, the processing that step S111~S113 is carried out to point each in historical data, obtain one group of smooth point, xiSmooth point
It is exactly xiFitting the match point (x on the straight line comei,yi), all the points are connected with short straight line, LOESS is obtained and returns song
Line obtains LOESS sharpening result according to LOESS regression curve.
4. the AGC system discrimination method according to claim 3 based on big data and Bayesian neural network, feature exist
In the weight in the step S112 is by cube weighting function T (u) definition:
Wherein, u=Δi(x)/Δ(q)(x), Δi(x)=| xi- x | indicate the distance of point x to xi, xi indicates appointing for historical data
Meaning data point, x indicate to remove x in historical dataiExcept other data points, Δ(q)(x) Δ is indicatedi(x) maximum value.
5. the AGC system discrimination method according to claim 3 based on big data and Bayesian neural network, feature exist
In including: according to the process that LOESS regression curve obtains LOESS sharpening result in the step S114
The quadratic sum for defining the difference that error function is match value and actual value, makes LOESS regression curve using gradient descent method
Approach y=θixi, y expression inputs or output data smoothed curve, θiIndicate xiThe slope for locating smoothed curve, to obtain LOESS
Sharpening result.
6. the AGC system discrimination method according to claim 2 based on big data and Bayesian neural network, feature exist
In zero initialization process specifically includes in the step S12:
Wherein, u*(k)、y*(k), u (k), y (k) respectively indicate the value after input data zero initializes, output data zero initializes
Rear value, input data zero initialize before value, output data zero initialize after value, N is constant, indicates the preceding N of access evidence
Initial value of the mean value of position as data.
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