CN110489717A - Barge heave movement prediction technique during offshore platform floating support mounting - Google Patents

Barge heave movement prediction technique during offshore platform floating support mounting Download PDF

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CN110489717A
CN110489717A CN201910597911.XA CN201910597911A CN110489717A CN 110489717 A CN110489717 A CN 110489717A CN 201910597911 A CN201910597911 A CN 201910597911A CN 110489717 A CN110489717 A CN 110489717A
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barge
sequence
heave movement
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heave
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张茂
章青
李银龙
吴云龙
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Tianjin University
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Abstract

The invention discloses barge heave movement prediction techniques during offshore platform floating support mounting, comprising the following steps: using MRU attitude transducer heave shift values several to barge continuous acquisition as heave movement data sample；Zero-mean is carried out to heave movement data sample and standardizes pretreatment operation, forms barge heave movement sequence；Data in heave movement sequence are divided into training set and test set；Discrete wavelet transformation and single branch reconstruct, low frequency component sequence and several high fdrequency component sequences after being reconstructed are carried out to training set；Support vector regression prediction model based on particle swarm optimization algorithm is established to respective vector sequence and carries out jump ahead prediction；The superposition of respective predicted value is merged and obtains final predicted value；It updates training set data repetition corresponding steps and obtains multiple predicted values；Predicted value and test set are subjected to data comparison.The method of the present invention can effectively improve the heave movement prediction step and accuracy of barge, guarantee the stability of prediction.

Description

Barge heave movement prediction technique during offshore platform floating support mounting
Technical field
The present invention relates to barges during a kind of motion forecast method more particularly to a kind of offshore platform floating support mounting to heave Motion forecast method.
Background technique
Floating support mounting method is a kind of huge buoyancy provided using barge container by offshore platform top module integral installation Method on to bottom bracing structure.Due to will receive the influence of the complex environments load such as wind, wave, stream, tidal bore in installation process, It translates, wave to which the upper module for inevitably leading to barge and on barge generates, upper and lower heave movement, wherein barge Heave movement up and down be affected to the installation of upper module and more difficult to control.Using such as Fig. 1 institute during floating support mounting The mode shown installs array Active Compensation oil cylinder 4 that is, between barge 1 and upper module 7, and Active Compensation oil cylinder 4 is connected to oil On cylinder support 3, intermediate barge support construction 5 and deck support unit 6 are mainly used to support firm upper module 7, in floating support The heave that barge generation can be compensated when installation in time by controlling the stretching motion of several Active Compensation oil cylinders 4 is displaced, thus Teetertottering for upper module 7 is efficiently reduced, the safety during floating support mounting is improved, reduces duty cycle and cost. In order to effectively carry out heaving movement compensation to upper module during floating support mounting, needing will by movement shortest time prediction Heave movement posture look-ahead within barge following more than ten seconds comes out, and makes to carry out heaving bit shift compensation accordingly in time Upper module held stationary during the installation process, wherein the heave displacement of barge can use MRU attitude transducer and be acquired, Its installation site is as shown in Figure 1.It is currently used for heave movement prediction technique mainly to have statistical fluctuation method, convolution method, card Kalman Filtering method, time series analysis method etc., wherein statistical fluctuation method needs to pre-process mass data, and with forecast The increase of time will lead to prediction error and significantly increase, and practice is difficult；Convolution method needs accurate measurement certain distance Ariyoshi wave height and ship itself response kernel function, this two o'clock is extremely difficult under the sea situation complicated in this way of deep-sea, because This algorithm practical application difficulty is also very big；Kalman filtering method of prediction also has certain limitation, calls time in advance shorter；When Between sequence analysis be a kind of more commonly used method, but the prediction technique is predicted for non-linear, non-stationary heave movement With certain limitation, and as there are serious lag and wild effect for the increase of predicted time.
Summary of the invention
It is an object of the invention to overcome the shortcomings of prior art, provide a kind of for during offshore platform floating support mounting Barge heave movement prediction technique, this method can effectively improve the heave movement prediction duration and accuracy of barge, protect Demonstrate,prove the stability of prediction.
In order to achieve the above object, the technical solution adopted by the present invention is that:
Barge heave movement prediction technique during offshore platform floating support mounting of the invention, comprising the following steps:
Step 1: interior for a period of time to barge using the MRU attitude transducer on barge in the sea area for carrying out floating support mounting N heave shift value of continuous acquisition is as heave movement data sample；
Step 2: carrying out zero-mean to the heave movement data sample of barge standardizes pretreatment operation, pretreatment is formed Barge heave movement sequence X afterwards, is represented by X={ x1,x2,…,xi,…,xn, wherein xiIndicate i-th of heave displacement Value standardizes pretreated result by zero-mean；
Step 3: by barge heave movement sequence X={ x1,x2,…,xi,…,xnIn data be divided into training set { x1, x2,…,xmAnd test set { xm+1,xm+2,…,xn, wherein m < n, and the data amount check m in training set accounts for about total data n's 80%；
Step 4: choosing wavelet basis function, the wavelet decomposition number of plies is determined, to the training set { x in heave movement sequence1, x2,…,xmDiscrete wavelet transformation and single branch reconstruct are carried out, low frequency component sequence and several high frequencies after single branch reconstruct can be obtained Vector sequence.
Wherein preferentially, dmey wavelet basis function is selected, Decomposition order is 3 layers, after carrying out discrete wavelet transformation and reconstruct Just first layer high fdrequency component sequence D is obtained1, second layer high fdrequency component sequence D2, third layer high fdrequency component sequence D3It is low with third layer Frequency component sequence C3, high frequency components sequence and low frequency component sequence can respectively indicate are as follows: D1={ d1,1,d1,2,…,d1,m, D2={ d2,1,d2,2,…,d2,m, D3={ d3,1,d3,2,…,d3,mAnd C3={ c3,1,c3,2,…,c3,m, d1,mIndicate first layer Than the m-th data in high fdrequency component sequence, similarly d2,m、d3,m；c3,mIndicate than the m-th data in third layer low frequency component sequence；
Step 5: being established respectively to the low frequency component sequence and high fdrequency component sequence that are obtained after reconstruct respective based on particle The Support vector regression prediction model (abbreviation PSO-SVR prediction model) of colony optimization algorithm, then using respectively established PSO-SVR prediction model carries out jump ahead prediction to the data in each vector sequence, thus under obtaining in respective vector sequence The predicted value of one dataWith
Step 6: the predicted value in the predicted value and each high fdrequency component sequence in obtained low frequency component sequence is carried out Superposition merges, and finally obtains the next data predicted value of barge heave movement, it may be assumed that
Step 7: updating training set data, that is, remove an oldest data x1, add newest prediction data Form new training set
Step 8: repeating step 4 to step 7, x in barge heave movement sequence can be successively obtainedm+1,…,xnIt is pre- Measured value
Step 9: by test set { xm+1,xm+2,…,xnIn data and corresponding predicted value carry out data comparison, to test Demonstrate,prove the accuracy of the barge heave movement prediction technique.
Compared with prior art, the invention has the following advantages:
The method of the present invention is by carrying out decomposition and reconstruction using discrete data of the wavelet analysis to barge heave movement, then It carries out the Support vector regression prediction based on particle swarm optimization algorithm respectively to each component, is superimposed and closes by each component predicted value And final predicted value is obtained, this method can barge be non-linear unstable when floating support mounting preferably suitable for practical sea situation The prediction of heave movement situation, and the convergence rate and prediction step of prediction algorithm are effectively improved, in barge heave movement Accuracy and stability with higher in prediction.
Detailed description of the invention
Fig. 1 is offshore platform floating support mounting process heave compensation model structure schematic diagram；
Fig. 2 is barge heave movement prediction technique flow chart during offshore platform floating support mounting of the invention.
Specific embodiment
The present invention will be described in detail in the following with reference to the drawings and specific embodiments.
As shown in Fig. 2, barge heave movement prediction technique during offshore platform floating support mounting of the invention, including it is following Step:
Step 1: interior for a period of time to barge using the MRU attitude transducer 2 on barge in the sea area for carrying out floating support mounting N heave shift value of continuous acquisition is as heave movement data sample；
Step 2: carrying out zero-mean to the heave movement data sample of barge standardizes pretreatment operation, pretreatment is formed Barge heave movement sequence X afterwards, is represented by X={ x1,x2,…,xi,…,xn, wherein xiIndicate i-th of heave displacement Value standardizes pretreated result by zero-mean；
Step 3: by barge heave movement sequence X={ x1,x2,…,xi,…,xnIn data be divided into training set { x1, x2,…,xmAnd test set { xm+1,xm+2,…,xn, wherein m < n, the data amount check m in preferred training set accounts for about total data n 80%；
Step 4: choosing wavelet basis function, the wavelet decomposition number of plies is determined, to the training set { x in heave movement sequence1, x2,…,xmDiscrete wavelet transformation and single branch reconstruct are carried out, the low frequency component sequence and several high fdrequency components after obtaining single branch reconstruct Sequence.
Wherein preferentially, dmey wavelet basis function is selected, Decomposition order is 3 layers, is just obtained after carrying out discrete wavelet transformation First layer high fdrequency component sequence d1, second layer high fdrequency component sequence d2, third layer high fdrequency component sequence d3With third layer low frequency component Sequence c3, but due to one layer of wavelet decomposition of every execution, the length of sequence will shorten to the half before decomposing, and Decomposition order is got over Greatly, the sequence length of acquisition is shorter, and after the reduction of data is unfavorable in sequence heave movement prediction model foundation, institute Become the data of each layer component as data amount check in original training set to need to carry out the reconstruct of small echo list branch, through too small After the reconstruct of wave list branch, first layer high fdrequency component sequence D can be obtained1, second layer high fdrequency component sequence D2, third layer high fdrequency component Sequence D3With third layer low frequency component sequence C3, high frequency components sequence and low frequency component sequence can respectively indicate are as follows: D1= {d1,1,d1,2,…,d1,m, D2={ d2,1,d2,2,…,d2,m, D3={ d3,1,d3,2,…,d3,mAnd C3={ c3,1,c3,2,…, c3,m, d1,mIndicate the than the m-th data in first layer high fdrequency component sequence, similarly d2,m、d3,m；c3,mIndicate third layer low frequency component Than the m-th data in sequence, and training set { x1,x2,…,xm}=C3+D1+D2+D3；Discrete wavelet transformation and the principle of reconstruct are specific Referring to " wavelet analysis and its application " of Sun Yankui.
Step 5: being established respectively to the low frequency component sequence and high fdrequency component sequence that are obtained after reconstruct respective based on particle The Support vector regression prediction model (abbreviation PSO-SVR prediction model) of colony optimization algorithm, then using respectively established PSO-SVR prediction model carries out jump ahead prediction to the data in each vector sequence, thus under obtaining in respective vector sequence The predicted value of one data.With first layer high frequency series component D1={ d1,1,d1,2,…,d1,mFor, it is predicted by PSO-SVR The predicted value of the m+1 data in first layer high fdrequency component sequence can be obtained after modelSimilarly obtainWithWherein, the foundation of PSO-SVR prediction model can be found in Wang Qian et al. " based on PSO-SVR's Danjiangkou annual flow forecast ".
Step 6: the predicted value in the predicted value and each high fdrequency component sequence in obtained low frequency component sequence is carried out Superposition merges, and finally obtains the next data predicted value of barge heave movement, it may be assumed that
Step 7: updating training set data, that is, remove an oldest data x1, add newest prediction data Form new training set
Step 8: repeating step 4 to step 7, x in barge heave movement sequence can be successively obtainedm+1,…,xnIt is pre- Measured value
Step 9: by test set { xm+1,xm+2,…,xnIn data and corresponding predicted value carry out data comparison, to test Demonstrate,prove the accuracy of the barge heave movement prediction technique.Wherein, data comparison method uses existing method: as using equal Square error (RMSE) method, i.e.,

Claims (2)

1. barge heave movement prediction technique during offshore platform floating support mounting, it is characterised in that the following steps are included:
Step 1: continuous in for a period of time to barge using the MRU attitude transducer on barge in the sea area for carrying out floating support mounting N heave shift value is acquired as heave movement data sample；
Step 2: carrying out zero-mean to the heave movement data sample of barge standardizes pretreatment operation, formed pretreated Barge heave movement sequence X, is expressed as X={ x1,x2,…,xi,…,xn, wherein xiIndicate i-th of heave shift value through zero passage Mean value standardizes pretreated result；
Step 3: by barge heave movement sequence X={ x1,x2,…,xi,…,xnIn data be divided into training set { x1, x2,…,xmAnd test set { xm+1,xm+2,…,xn, wherein m < n；
Step 4: choosing wavelet basis function, the wavelet decomposition number of plies is determined, to the training set { x in heave movement sequence1,x2,…, xmDiscrete wavelet transformation and single branch reconstruct are carried out, the low frequency component sequence and several high fdrequency component sequences after obtaining single branch reconstruct；
Step 5: being established respectively to the low frequency component sequence and high fdrequency component sequence that are obtained after reconstruct respective excellent based on population Change the Support vector regression prediction model of algorithm, then using respective established PSO-SVR prediction model to each component sequence Data in column carry out jump ahead prediction, to obtain the predicted value of next data in respective vector sequence；
Step 6: the predicted value in the predicted value and each high fdrequency component sequence in obtained low frequency component sequence is overlapped Merge, obtains the next data predicted value of barge heave movement；
Step 7: updating training set data, remove an oldest data x1, add newest prediction dataIt is formed new Training set
Step 8: repeating step 4 to step 7, x in barge heave movement sequence can be successively obtainedm+1,…,xnPredicted value
Step 9: by test set { xm+1,xm+2,…,xnIn data and corresponding predicted value carry out data comparison, with verifying should The accuracy of barge heave movement prediction technique.
2. barge heave movement prediction technique, feature exist during offshore platform floating support mounting according to claim 1 In: the wavelet basis function chosen in the step four is dmey wavelet basis function, and Decomposition order is 3 layers, carries out discrete wavelet It decomposes and obtains first layer high fdrequency component sequence D after reconstructing1, second layer high fdrequency component sequence D2, third layer high fdrequency component sequence D3 With third layer low frequency component sequence C3。
CN201910597911.XA 2019-07-04 2019-07-04 Barge heave movement prediction technique during offshore platform floating support mounting Pending CN110489717A (en)

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CN112762935A (en) * 2020-12-23 2021-05-07 赫星(厦门)电子有限公司 Positioning method and system based on hull attitude monitoring
CN113942622A (en) * 2021-11-19 2022-01-18 博迈科海洋工程股份有限公司 Motion compensation method suitable for FPSO upper module lifting installation process
CN114894043A (en) * 2022-03-30 2022-08-12 北京航天飞腾装备技术有限责任公司 Precise guidance ammunition attitude filtering method and system based on wavelet packet transformation

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CN107316101A (en) * 2017-06-02 2017-11-03 西南交通大学 A kind of wind speed forecasting method selected in advance based on wavelet decomposition and component
CN109657709A (en) * 2018-12-06 2019-04-19 湖北博华自动化系统工程有限公司 A kind of equipment fault prediction technique based on particle group optimizing support vector regression

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CN105117593A (en) * 2015-08-18 2015-12-02 河海大学 Wavelet transform and particle swarm optimized grey model-based short-term wind speed forecasting method
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Publication number Priority date Publication date Assignee Title
CN112762935A (en) * 2020-12-23 2021-05-07 赫星(厦门)电子有限公司 Positioning method and system based on hull attitude monitoring
CN113942622A (en) * 2021-11-19 2022-01-18 博迈科海洋工程股份有限公司 Motion compensation method suitable for FPSO upper module lifting installation process
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