CN103149278A - Correction method for on-line monitoring noisy data of oil chromatography - Google Patents

Abstract

The invention relates to a correction method for on-line monitoring noisy data of oil chromatography. The method includes the following steps: step 1, collecting data of off-line tests and on-line monitoring of the oil chromatography; step 2, obtaining an optimal combination of significant parameters in a regression model of a support vector machine through a firefly algorithm; step 3, training the support vector machine with the small amount of accurate off-line test data of the oil chromatography obtained, and obtaining the regression model of the support vector machine; step 4, initializing a permissible deviation radius h of the on-line monitoring data, calculating a piecewise function between the off-line tests, and judging whether the on-line monitoring data of the oil chromatography is in a permissible error range of the model; step 5, correcting the on-line data; and step 6, according to the result of correction feedback of the on-site data, adjusting the parameters in the model. When the method is used for correction of the on-line data of the oil chromatography, the effect is stable, the result is accurate, the time is short, and the real-time performance is good, and the method is very suitable for correction of the one-site on-line data of the oil chromatography.

Description

The bearing calibration of a kind of oil chromatography on-line monitoring noise data

Technical field

The invention belongs to converting equipment on-line monitoring technique field, be applied in the noise data trimming process of transformer online monitoring equipment specifically a kind of oil chromatography on-line monitoring noise data bearing calibration.

Background technology

The transformer oil chromatographic on-line monitoring can in time be grasped the operation conditions of transformer, finds and follow the tracks of the latency fault, for the reliability service of transformer provides safeguard.But be subject to the impact of the factors such as environment temperature, humidity and monitoring equipment self error due to the oil chromatography on-line monitoring, may there be distortion in online data, needs to carry out the data schools before state evaluation and fault diagnosis.At present, Chinese scholars has been done a large amount of research work and has been proposed some algorithms the data Correction Problems.Principle component regression can be removed noise data effectively, but the error of match is large, and calibration accuracy is low; The neural network algorithm fitting effect is good, but data volume is when large, and the training time is long, has the problem of " crossing study ".

For the poor situation of present oil chromatography online data calibration result, the present invention proposes the method based on the Data correction of firefly support vector machine.At first by the firefly algorithm, the important parameter that affects the support vector machine performance is optimized.Then calculate the piecewise function between the oil chromatography off-line data, when online data exceeds the scope of piecewise function error permission, think that online data is abnormal.Utilize minority accurately the oil chromatography off-line data support vector machine regression model is trained, when by the support vector machine regression model, abnormal online data is proofreaied and correct when abnormal appears in online data.The algorithm that the present invention proposes can be applied among the oil chromatography on-Line Monitor Device, Condition Monitoring Data to transformer oil chromatographic carries out on-line calibration, the data that make oil chromatography on-Line Monitor Device output are true and accurate more, effectively reject the bad point data, under the prerequisite that guarantees data validity, improve the availability of data; Also can be applied in the master system of power transmission and transformation equipment state monitoring, the data of oil chromatography on-line monitoring be carried out verification, reasonably tell active data, propose the bad point data, the oil chromatography data be played the effect of filtration.The data that this algorithm process of process is crossed can directly apply to fault diagnosis and the state evaluation of the power transmission and transforming equipments such as transformer, thereby directly instruct production run, repair based on condition of component and fault diagnosis work, improve greatly production cost and management level, the industry developments such as advanced state monitoring, repair based on condition of component and intelligent O﹠M produce huge economic benefit and social benefit.

Summary of the invention

In order to solve the above-mentioned problems in the prior art, the invention provides a kind of oil chromatography on-line monitoring noise data bearing calibration that is suitable for transformer.

The present invention has done brand-new design on the algorithm of Data correction, concrete technical scheme is as follows:

The bearing calibration of a kind of oil chromatography on-line monitoring noise data, its method comprises the following steps:

Step 1), collection oil chromatography off-line testing and online monitoring data;

Step 2), pass through the optimum combination of important parameter in the supported vector machine regression model of firefly algorithm;

Step 3), utilize minority that step 1) obtains accurately oil chromatography off-line testing data support vector machine is trained, supported vector machine regression model;

Step 4), initialization online monitoring data allow to depart from radius h, calculate the piecewise function between off-line testing, judge that the data of oil chromatography on-line monitoring are whether within the error range of model permission; If, think that online data is normal; Otherwise, think that online data is abnormal;

Step 5), online data is proofreaied and correct: judge one by one whether online data normal, if data exception, by the support vector machine Regression Model Simulator should be constantly data, replace abnormal data with match value; Otherwise, think that the oil chromatography online monitoring data is normal, do not need to proofread and correct;

Step 6), according to the result of field data correction feedback, the parameter in model is adjusted.

In above scheme, described step 2) concrete steps in are as follows:

Step 2.1), initialization fluorescein volatility coefficient ρ, fluorescein enhancer γ, sensing range r _s, neighborhood rate of change β, neighbours' threshold value n _t, the position of moving step length s and every firefly;

Step 2.2), need in support vector machine to determine the span of Optimal Parameters: determine wrong penalty factor, the span of insensitive parameter ε and nuclear parameter σ optimal value;

Step 2.3), in every group of parameter value scope, choose at random a class value as the position of firefly individuality; As fitness, error is less with the error of support vector machine match value and actual value, and performance that should the group parameter is better, and fitness is larger;

Step 2.4), calculate fluorescein concentration, decision domain scope and the neighbours' of each firefly fluorescein concentration, determine the moving direction of firefly and move forward by neighbours' fluorescein concentration;

Step 2.5), judge whether the firefly algorithm reaches end condition; If reach, choose the parameter that optimum solution is support vector machine; Otherwise, turn step 2.4).

In above scheme, described step 2.1) in, because the important parameter that needs in support vector machine to optimize is wrong penalty factor, insensitive parameter ε and nuclear parameter σ optimal value, in firefly, colony is expressed as X=((C ₁, ε ₁, σ ₁), (C ₂, ε ₂, σ ₂) ..., (C _N, ε _N, σ _N)).

In above scheme, in described step 4), suppose t constantly _iAnd t _j(t _i≠ t _j) off-line data be respectively y _iAnd y _j, these two constantly the piecewise function between off-line data be

If the radius that between twice off-line testing, piecewise function allows to depart from is h, t constantly _iAnd t _jBetween twice off-line testing, the upper limit function of online monitoring data is

The lower limit function of online monitoring data is

If online data has exceeded the upper limit or lower limit that the off-line data piecewise function allows, think that online monitoring data is abnormal, need to proofread and correct.

In above scheme, in described step 6), the error sum of squares of models fitting off-line data is less, and model is more accurate.

In above scheme, in described step 6), the quadratic sum of two data difference that abnormal data corrected value and on-line monitoring are adjacent is less, and the data after proofreading and correct are got over stable and continuous, and calibration result is better.

In above scheme, in described step 6), the training time of model is shorter, and model is more suitable carries out real time correction to online data.

By the engineering application attestation, the present invention carries out oil chromatography online data calibration result exactly, and the time, short real-time was good, is fit to very much the oil chromatography online data at scene is proofreaied and correct.

Description of drawings

Fig. 1 is the schematic diagram of embodiment of the present invention oil chromatography online data means for correcting;

Fig. 2 is the structural drawing that embodiment of the present invention oil chromatography online data is proofreaied and correct;

Fig. 3 is the process flow diagram of embodiment of the present invention firefly algorithm optimization support vector machine important parameter;

Fig. 4 is the process flow diagram that embodiment of the present invention support vector machine regression model is proofreaied and correct online noise data.

Embodiment

The invention will be further described below in conjunction with the drawings and specific embodiments.

Fig. 3 is the process flow diagram of embodiment of the present invention firefly algorithm optimization support vector machine important parameter; Fig. 4 is the process flow diagram that embodiment of the present invention support vector machine regression model is proofreaied and correct online noise data.As Fig. 3, Fig. 4, the bearing calibration of a kind of oil chromatography on-line monitoring noise data, its method comprises the following steps:

Step 1), collection oil chromatography off-line testing and online monitoring data;

Step 2), pass through the optimum combination of important parameter in the supported vector machine regression model of firefly algorithm;

Step 3), utilize minority that step 1) obtains accurately oil chromatography off-line testing data support vector machine is trained, supported vector machine regression model;

Step 4), initialization online monitoring data allow to depart from radius h, calculate the piecewise function between off-line testing, judge that the data of oil chromatography on-line monitoring are whether within the error range of model permission; If, think that online data is normal; Otherwise, think that online data is abnormal;

Step 5), online data is proofreaied and correct: judge one by one whether online data normal, if data exception, by the support vector machine Regression Model Simulator should be constantly data, replace abnormal data with match value; Otherwise, think that the oil chromatography online monitoring data is normal, do not need to proofread and correct;

Step 6), according to the result of field data correction feedback, the parameter in model is adjusted.

The one-piece construction of oil chromatography online data correcting algorithm as shown in Figure 2, this algorithm adopts the off-line data verification, proofreaies and correct the thinking of online monitoring data, build off-line oil chromatography Sample Storehouse by the data message under the various situations of collecting the oil chromatography off-line testing, optimize the parameter of on-line testing algorithm by offline database.The data that on-Line Monitor Device is collected are passed through the online data checking algorithm to data analysis, algorithm is not done special processing to normal data, algorithm is proofreaied and correct irrational noise data of on-line equipment, and the result that then will proofread and correct is sent into follow-up senior application relevant data analysis and diagnosis.

The principle of oil chromatography online data means for correcting is the module of online data correction as shown in Figure 1 in frame, it obtains data by data acquisition module, and data results is turned back to display interface, thereby the guide field personnel carry out work.Affect comparatively accurately the important parameter of performance in the off-line data Support Vector Machines Optimized, then to the model training after Optimal Parameters, when judging that by piecewise function online data is abnormal, be somebody's turn to do data constantly by the support vector machine Regression Model Simulator, replace abnormal data with match value, if the judgement online data is not normally proofreaied and correct.

The algorithm that the oil chromatography online data is proofreaied and correct is described in detail, and algorithm mainly is divided into two parts.First is by the optimum combination of important parameter in the supported vector machine regression model of firefly algorithm, and as shown in Figure 3, the concrete steps of this some algorithm are as follows:

Step 1: the parameter of initialization firefly algorithm: initialization fluorescein volatility coefficient ρ, fluorescein enhancer γ, sensing range r _s, neighborhood rate of change β, neighbours' threshold value n _t, the position of moving step length s and every firefly;

Step 2: span that need in support vector machine to determine Optimal Parameters; Determine wrong penalty factor, the span of insensitive parameter ε and nuclear parameter σ optimal value;

Step 3: in every group of parameter value scope, choose at random a class value as the position of firefly individuality.As fitness, error is less with the error of support vector machine match value and actual value, and performance that should the group parameter is better, and fitness is larger;

Step 4: calculate fluorescein concentration, decision domain scope and the neighbours' of each firefly fluorescein concentration, determine the moving direction of firefly and move forward by neighbours' fluorescein concentration;

Step 5: judge whether the firefly algorithm reaches end condition; If reach, choose the parameter that optimum solution is support vector machine; Otherwise, turn step 4).

Because the important parameter that needs in support vector machine to optimize is wrong penalty factor, insensitive parameter ε and nuclear parameter σ optimal value, so in firefly, colony is expressed as follows:

X＝((C ₁，ε ₁，σ ₁)，(C ₂，ε ₂，σ ₂)，…，(C _N，ε _N，σ _N))……(1)。

The fluorescein concentration of i firefly is as follows:

l _i(t)＝(1-ρ)l _i(t-1)+γJ(x _i(t))……(2)，

Decision domain scope more new formula is as follows:

$r_d^i(t+1)=\min \{r_s,\max \{0,r_d^i\left(t\right)+\mathrm{\β}(n_t-|N_i\left(t\right)\left|\right)\left\}\right\}......\left(3\right),$

The t moment neighborhood of i firefly is as follows:

$N_i\left(t\right)=\{j:||x_j\left(t\right)-x_i\left(t\right)||<r_d^i\left(t\right);l_i\left(t\right)<l_j\left(t\right)\}......\left(4\right).$

Firefly is decided its moving direction according to the fluorescein concentration of each firefly in its neighborhood in motion process, the probability that moves of i firefly j firefly in its neighborhood is as follows constantly for t:

$P_{\mathrm{ij}}\left(t\right)=\frac{l_j\left(t\right)-l_i\left(t\right)}{{\mathrm{\&Sigma;}}_{k\&Element;N_i\left(t\right)}{l}_k\left(t\right)-l_i\left(t\right)}......\left(5\right).$

I firefly is as follows in the position in the t+1 moment:

When firefly algorithm end condition being set being iterations and surpassing 1000 times, algorithm withdraws from circulation.

Second portion is proofreaied and correct irrational noise data by the support vector machine regression model that trains, and as shown in Figure 4, the concrete steps of this some algorithm are as follows:

Step 1: collect oil chromatography off-line testing and online monitoring data;

Step 2: the parameter of Support Vector Machines Optimized regression model.Obtain the optimum combination of important parameter in regression model by the firefly algorithm;

Step 3: utilize minority accurately oil chromatography off-line testing data support vector machine is trained, supported vector machine regression model;

Step 4: the initialization online monitoring data allow to depart from radius h, calculate the piecewise function between off-line testing, judge that the data of oil chromatography on-line monitoring are whether within the error range of model permission.If, think that online data is normal; Otherwise, think that online data is abnormal;

Step 5: online data is proofreaied and correct.Judge one by one whether online data is normal, if data exception is somebody's turn to do data constantly by the support vector machine Regression Model Simulator, replace abnormal data with match value; Otherwise, think that the oil chromatography online monitoring data is normal, do not need to proofread and correct;

Step 6: according to the result of field data correction feedback, the parameter in model is adjusted, made method in the present invention to the better effects if of the online noise compensation of oil chromatography.

It is as follows that support vector machine returns the function of estimating:

F (x)=ω φ (x)+b ... (7), the support vector machine employing minimizes structure and determines ω and b, namely

$\min R_{\mathrm{str}}=\frac{1}{2}{\left|\right|\mathrm{\&omega;}\left|\right|}^2+{\mathrm{CR}}_{\mathrm{emp}}......\left(8\right),$

Wherein

And L _ε(x, y-f (x))=max{0, | y-f (x)-ε } ... (10), find the solution the function that formula (8) finally can supported vector machine regression model as follows:

$f\left(x\right)=\underset{i=1}{\overset{l}{\mathrm{\&Sigma;}}}({\mathrm{\&alpha;}}_i^{*}-{\mathrm{\&alpha;}}_i)K(x_i,x)+b......\left(11\right),$

Select the most general radial basis RBF kernel function of usable range as follows in regression model:

$K(x_i,x)=\exp [-\frac{{\left|\right|x_i-x\left|\right|}^2}{2{\mathrm{\&sigma;}}^2}]......\left(12\right).$

Piecewise function by off-line data judges whether online data is abnormal, supposes t constantly _iAnd t _j(t _i≠ t _j) off-line data be respectively y _iAnd y _j, these two constantly the piecewise function between off-line data be

Suppose that the radius that between twice off-line testing, piecewise function allows to depart from is h, t constantly _iAnd t _jBetween twice off-line testing, the upper limit function of online monitoring data is

The lower limit function of online monitoring data is

If online data has exceeded the upper limit or lower limit that the off-line data piecewise function allows, think that online monitoring data is abnormal, need to proofread and correct.

The error sum of squares of models fitting off-line data is less, and model is more accurate.The quadratic sum of two data difference that abnormal data corrected value and on-line monitoring are adjacent is less, and the data after proofreading and correct are got over stable and continuous, and calibration result is better.The training time of model is shorter, and model is more suitable carries out real time correction to online data.By the engineering application attestation, the present invention carries out oil chromatography online data calibration result exactly, and the time, short real-time was good, is fit to very much the oil chromatography online data at scene is proofreaied and correct.

Claims (4)

1. oil chromatography on-line monitoring noise data bearing calibration, it is characterized in that: its method comprises the following steps:

Step 1), collection oil chromatography off-line testing and online monitoring data;

Step 2), pass through the optimum combination of important parameter in the supported vector machine regression model of firefly algorithm;

Step 3), utilize minority that step 1) obtains accurately oil chromatography off-line testing data support vector machine is trained, supported vector machine regression model;

Step 4), initialization online monitoring data allow to depart from radius h, calculate the piecewise function between off-line testing, judge that the data of oil chromatography on-line monitoring are whether within the error range of model permission; If, think that online data is normal; Otherwise, think that online data is abnormal;

Step 5), online data is proofreaied and correct: judge one by one whether online data normal, if data exception, by the support vector machine Regression Model Simulator should be constantly data, replace abnormal data with match value; Otherwise, think that the oil chromatography online monitoring data is normal, do not need to proofread and correct;

Step 6), according to the result of field data correction feedback, the parameter in model is adjusted.

2. oil chromatography on-line monitoring noise data as claimed in claim 1 bearing calibration, it is characterized in that: the concrete steps described step 2) are as follows:

Step 2.1), initialization fluorescein volatility coefficient ρ, fluorescein enhancer γ, sensing range r _s, neighborhood rate of change β, neighbours' threshold value n _t, the position of moving step length s and every firefly;

Step 2.2), need in support vector machine to determine the span of Optimal Parameters: determine wrong penalty factor, the span of insensitive parameter ε and nuclear parameter σ optimal value;

Step 2.3), in every group of parameter value scope, choose at random a class value as the position of firefly individuality; With the error of support vector machine match value and actual value as fitness;

Step 2.4), calculate fluorescein concentration, decision domain scope and the neighbours' of each firefly fluorescein concentration, determine the moving direction of firefly and move forward by neighbours' fluorescein concentration;

Step 2.5), judge whether the firefly algorithm reaches end condition; If reach, choose the parameter that optimum solution is support vector machine; Otherwise, turn step 2.4).

3. oil chromatography on-line monitoring noise data as claimed in claim 2 bearing calibration, it is characterized in that: described step 2.1), because the important parameter that needs in support vector machine to optimize is wrong penalty factor, insensitive parameter ε and nuclear parameter σ optimal value, in firefly, colony is expressed as X=((C ₁, ε ₁, σ ₁), (C ₂, ε ₂, σ ₂) ..., (C _N, ε _N, σ _N)).

4. oil chromatography on-line monitoring noise data as claimed in claim 1 bearing calibration is characterized in that: in described step 4), suppose t constantly _iAnd t _j(t _i≠ t _j) off-line data be respectively y _iAnd y _j, these two constantly the piecewise function between off-line data be $f_{\mathrm{ij}}\left(t\right)=\frac{y_j-y_i}{t_j-t_i}t+\frac{y_i{t}_j-y_j{t}_i}{t_j-t_i};$

If the radius that between twice off-line testing, piecewise function allows to depart from is h, t constantly _iAnd t _jBetween twice off-line testing, the upper limit function of online monitoring data is

The lower limit function of online monitoring data is

If online data has exceeded the upper limit or lower limit that the off-line data piecewise function allows, think that online monitoring data is abnormal, need to proofread and correct.

Images