CN103323757A - Power distribution cabinet gas insulation intensity prediction device and method - Google Patents

Abstract

The invention discloses a power distribution cabinet gas insulation intensity prediction device and method and belongs to the technical field of power distribution cabinets. The device comprises a current sensor, a voltage sensor, a gas pressure sensor, an insulation tester, a data collecting chip, a central processor, an industrial personal computer and a wireless communication module. The intensity health condition of a power distribution cabinet can be known, premature or unnecessary power-cut tests or maintaining is reduced, maintaining when maintaining is needed is achieved, the reliability and the economy of a power system are improved obviously, prediction and analysis are carried out on the gas insulation intensity of the novel user inner power distribution cabinet developed and produced by a production enterprise by itself, and maintaining personnel can carry out maintaining conveniently.

Description

A kind of power distribution cabinet gas-insulated prediction of strength device and method

Technical field

Belong to the power distribution cabinet technical field, particularly a kind of power distribution cabinet gas-insulated prediction of strength device and method.

Background technology

Relevant statistics shows, the power distribution network maintenance cost is over half is flower on power distribution cabinet, and 30% be light maintenance and regular maintenance for power distribution cabinet wherein; In addition according to statistics, 15% power distribution cabinet fault is because due to the incorrect maintenance, the overhaul of power distribution cabinet is disintegrated fully, both time-consuming, expense is very high again, can reach 1/3～1/2 of whole power distribution cabinet, and disintegrate and ressemble and to cause a lot of defectives, consequent accident example is too numerous to enumerate especially, which parts (or critical elements) for power distribution cabinet, how long operation needs to change, and is still the problem of a dispute, in fact in relatively more conservative at present scheduled overhaul, it is still functional when a lot of years rear renewals of many parts operations occur often, and owing to not in time not finding, because there is defective in dielectric strength, the power distribution cabinet short trouble often occurs and causes the situation of power distribution network accident also to happen occasionally.

Summary of the invention

For the deficiencies in the prior art, the present invention proposes a kind of power distribution cabinet gas-insulated prediction of strength device and method, reduces too early or unnecessary power failure test and maintenance to reach, and improves the purpose of the convenience of Power System Reliability, economy and maintenance.

A kind of power distribution cabinet gas-insulated prediction of strength device comprises current sensor, voltage sensor, baroceptor, insulation tester, data acquisition chip, central processing unit, industrial computer and wireless communication module; Wherein, one road input end of the output terminal connection data acquisition chip of described current sensor; Another road input end of the output terminal connection data acquisition chip of voltage sensor; The another road input end of the output terminal connection data acquisition chip of baroceptor; The another road input end of the output terminal connection data acquisition chip of insulation tester; The output terminal of data acquisition chip connects the input end of central processing unit, and the two-way output terminal of central processing unit connects respectively the input end of industrial computer and the input end of wireless communication module.

The method that adopts power distribution cabinet gas-insulated prediction of strength device to predict may further comprise the steps:

Step 1, gather insulating coefficient, operating voltage, dropout current and four parameters of air pressure of power distribution cabinet, and above-mentioned four parameters are sent in the data acquisition chip;

Step 2, data acquisition chip carry out analog to digital conversion to four parameters that gather, and four parameters after the analog to digital conversion are sent in the data processor;

Step 3, data processor are predicted power distribution cabinet gas-insulated intensity according to four parameters that gather;

Step 3-1, employing empirical mode decomposition method are carried out resolution process to the time series that insulating coefficient, operating voltage, dropout current and four parameters of air pressure by power distribution cabinet consist of;

Step 3-2, employing recurrent neural network method are predicted decomposing rear time series, and based on Phase-space Reconstruction, time series after decomposing is embedded in the phase space, employing mutual information method is determined the time delay in the phase space, adopt embedding dimension algorithm to determine the correlation dimension of phase space, and then obtain a plurality of different anticipation components;

Step 3-3, the Dynamical Recurrent Neural Networks structure is set, the a plurality of different anticipation component input Dynamical Recurrent Neural Networks that step 3-2 obtains is trained, and adopt the method for linear combination that all the different components after training are carried out linear combination, namely obtain power distribution cabinet gas-insulated prediction of strength function;

Step 4, data processor are sent to the power distribution cabinet gas-insulated intensity of predicting in the industrial computer and store, and are sent to the remote dispatching terminal by wireless communication module, so that the maintenance personal in time overhauls.

The described employing empirical mode decomposition of step 3-1 method is carried out resolution process to the time series that insulating coefficient, operating voltage, dropout current and four parameters of air pressure by power distribution cabinet consist of, and specifically may further comprise the steps:

Step 3-1-1, determine the Local Extremum that time series signal is all, and adopt the cubic spline interpolation curve that all Local modulus maximas are coupled together to form the coenvelope line;

Step 3-1-2, adopt the cubic spline interpolation curve that all local minizing points are coupled together to form the lower envelope line, determine all data points of envelope envelope according to envelope up and down;

Step 3-1-3, the mean value of envelope is designated as m up and down ₁, obtain difference h ₁:

h ₁＝z(t)-m ₁ (1)

Wherein, z (t)=[z ₁(t), z ₂(t) ..., z ₄(t)]; z ₁(t) data of the 1st input quantity in four parameters constantly gathering of expression t; z ₂(t) data of the 2nd input quantity in four parameters constantly gathering of expression t; z ₄(t) data of the 4th input quantity in four parameters constantly gathering of expression t;

If h ₁Maximal value h _MaxWith minimum value h _MinDifference less than a predefined value ε, ε≤0.01

|h _max-h _min|≤ε (2)

H then ₁A mode component for z (t); Otherwise, execution in step 3-1-4;

Step 3-1-4, with h ₁As another primary data, repeating step 3-1-1 recirculates k time, until h to step 3-1-3 _k=h _K-1-m _kMiddle h _kSatisfy the condition of formula (2), note c ₁=h _k, c then ₁First mode component for signal z (t);

Step 3-1-5, with c ₁From z (t), separate, obtain z (t) remainder r ₁:

r ₁＝z(t)-c ₁ (3)

With r ₁To step 3-1-4, n recirculates as another primary data repeating step 3-1-1 _tInferior, obtain the n of signal z (t) _tIndividual mode component;

When the remainder of z (t) was a monotonic quantity, circulation finished, and signal z (t) is decomposed into n _tIndividual mode component

With a residual

Sum, that is:

$z\left(t\right)=\underset{{\mathrm{<figure-callout\; id="1"\; label="i"\; filenames="HDA00003389249300021.png,HDA00003389249300032.png"\; state="\{\{state\}\}">i</figure-callout>}}_1=1}{\overset{n_t}{\mathrm{\&Sigma;}}}{c}_{i_1}+r_{n_t}---\left(4\right)$

Wherein, component

Represent the characteristic signal of the different time that comprises among the original signal z (t), established x (t), and order

X (t) has represented the central tendency of signal z (t).

Step 3-2 is described based on Phase-space Reconstruction, time series after decomposing is embedded in the phase space, time series x (t) after being about to decompose is embedded into phase space X (t)={ x (t), x (t-τ), ..., x (t-(m-1) τ) } in, line number is the number of parameters that gathers in the described phase space, columns is correlation dimension;

Wherein, the dynamics state of etching system during X (t) expression t, τ is time delay, τ=1,2 ..., n ₁, n ₁Be natural number; M is the dimension of embedded space;

Described employing recurrent neural network method is predicted decomposing rear time series, determines and certainly exists function F () in the phase space so that the time series state after the time-delay And satisfy between the current state X (t):

$\hat{x}(t+\mathrm{\&tau;})=F\left(X\left(t\right)\right)---\left(14\right)$

F () is power distribution cabinet gas-insulated prediction of strength function to be sought;

And adopt interpolation method that sequence is expanded, obtain a plurality of different anticipation components:

$\hat{x}(t^{\&prime;}+(\mathrm{\&lambda;}+1)p)=F(x^{\&prime;}\left(t^{\&prime;}\right),x^{\&prime;}(t^{\&prime;}-\mathrm{\&tau;}),...,x^{\&prime;}(t^{\&prime;}-(m-1)\mathrm{\&tau;}))---\left(16\right)$

Wherein, λ is the interpolation multiple, t ' is the moment after the interpolation, p is the prediction step number, x ' (t ') is the time series after the interpolation, x ' (t '-τ) be the time series that postpones the τ time after the interpolation, x ' (t '-(m-1) τ) be the time series of delay (m-1) τ time after the interpolation.

Advantage of the present invention:

A kind of power distribution cabinet gas-insulated of the present invention prediction of strength device and method, can understand the dielectric strength health status of power distribution cabinet, reduce too early or unnecessary power failure test and maintenance, accomplish to answer Xiu Zexiu, Power System Reliability and economy have been significantly improved, manufacturing enterprise is researched and developed voluntarily and makes the Novel indoor power distribution cabinet gas-insulated intensity of production and carry out forecast analysis, can the convenient for maintaining personnel overhaul.

Description of drawings

The power distribution cabinet gas-insulated prediction of strength apparatus structure block diagram of Fig. 1 an embodiment of the present invention;

The power distribution cabinet gas-insulated prediction of strength device work schematic diagram of Fig. 2 an embodiment of the present invention;

A/D converter and the processor circuit schematic diagram of the power distribution cabinet gas-insulated prediction of strength device of Fig. 3 an embodiment of the present invention;

The level-conversion circuit schematic diagram of Fig. 4 an embodiment of the present invention;

The power distribution cabinet gas-insulated prediction of strength method general flow chart of Fig. 5 an embodiment of the present invention;

The Dynamical Recurrent Neural Networks structural representation that adopts in Fig. 6 an embodiment of the present invention;

The employing EMD_DRNN mixture model structure of Fig. 7 an embodiment of the present invention is carried out the process flow diagram of power distribution cabinet gas-insulated prediction of strength;

The prediction dielectric strength curve of Fig. 8 an embodiment of the present invention and actual insulation intensity.

Embodiment

Below in conjunction with accompanying drawing an embodiment of the present invention is described further.

As shown in Figure 1, a kind of power distribution cabinet gas-insulated prediction of strength device comprises current sensor, voltage sensor, baroceptor, insulation tester, data acquisition chip, central processing unit, industrial computer and wireless communication module; Wherein, one road input end of the output terminal connection data acquisition chip of described current sensor; The another road input end of the output terminal connection data acquisition chip of voltage sensor; The another road input end of the output terminal connection data acquisition chip of baroceptor; The another road input end of the output terminal connection data acquisition chip of insulation tester; The output terminal of data acquisition chip connects the input end of central processing unit, and the two-way output terminal of central processing unit connects respectively the input end of industrial computer and the input end of wireless communication module.

In the embodiment of the invention, the power distribution cabinet model is ZGSM, and this power distribution cabinet used 5 years; As shown in Figure 2, by to power distribution cabinet gas-insulated prediction of strength, the result who predicts is sent by wireless module, be sent to the remote dispatching terminal by 3G network.

Signal acquisition module in the embodiment of the invention comprises voltage transformer (VT), current transformer, baroceptor, insulation tester, wherein, voltage sensor adopts JDG4-1.5 500/50 model, current sensor adopts LZJC-10Q 1000/5 model current transformer, be installed to the switch cubicle end of incoming cables, insulation tester is selected DL09-SDM50, is positioned on the insulation shell of power distribution cabinet and gathers insulating coefficient, baroceptor is selected the PT206 model, is placed in the power distribution cabinet.

In the embodiment of the invention, as shown in Figure 3, described data acquisition chip, i.e. A/D converter is selected 12 bits serial A of TLC2044 model/D converter, and wherein, AIN0～AIN10 is analog input end; CS is sheet choosing end; DIN is the serial data input end; DOUT is the ternary serial output terminal of A/D transformation result; EOC is the EOC end; CLK is the I/O clock; REF+ is positive reference voltage terminal; REF-is negative reference voltage terminal; VCC is power supply; GND is ground.

In the embodiment of the invention, as shown in Figure 3, it is the single-chip microcomputer of CS8051 that central processing unit is selected model, and the serial port that uses single-chip microcomputer to carry can be realized the serial communication with industrial computer; The COM1 that present PC provides, COM2 adopts the RS-232 interface standard, and RS-232 comes the presentation logic state with generating positive and negative voltage, come the regulation of presentation logic state different from TTL with high-low level, therefore, in order to be connected with computer interface or with the TTL device (such as single-chip microcomputer) of terminal, must between RS-232 and TTL circuit, carry out the conversion of level and logical relation, as shown in Figure 4, translation circuit in the present embodiment is selected MAX232, this device comprises 2 drivers, 2 receivers and a voltage generator circuit, this voltage generator circuit provides the TIA/EIA-232-F level, this device meets the TIA/EIA-232-F standard, each receiver becomes 5V TTL/CMOS level with the TIA/EIA-232-F level conversion, each generator becomes the TIA/EIA-232-F level with the TTL/CMOS level conversion, single-chip microcomputer is the core of whole device, serial a/d converter TLC2044 gathers the simulating signal of input, sampling resolution, ALT-CH alternate channel and output polarity are selected by software, owing to being the serial input structure, can save 51 series monolithic I/O resources, the data communication device of single-chip microcomputer collection is crossed serial ports RXD, TXD pin (10,11 pins) convert realization transmission between RS232 level and industrial computer to through MAX232.

In the embodiment of the invention, industrial computer is selected and is adopted UNO-3072 Series P entium M/Celeron M built-in industrial control machine.

In the embodiment of the invention, wireless communication module adopts H7000 series wireless communication system.

As shown in Figure 3, the output terminal of voltage transformer (VT), current transformer, frequency sensor, insulation tester is connected respectively to input end AIN0～AIN3 of A/D converter TLC2044, output terminal EOC, the CLK of A/D converter TLC2044, DIN, DOUT are connected respectively to P10, P11, P12, the P13 of single-chip microcomputer, 10 pins (RXD) of single-chip microcomputer CS8051,11 pins (TXD) are connected with the T2in pin with the R2out pin of translation circuit MAX232, and the industrial computer input end is connected input end and is connected with single-chip microcomputer output terminal P00, P01 successively with wireless communication module; The electric information of switch cubicle carries out synchronized sampling, maintenance, A/D conversion via corresponding device by sampling A/D chip, become digital signal, sending into single-chip microcomputer calculates with data and processes, link to each other with industrial computer and data are delivered to transport module by communication interface, for ready with the remote dispatching communication.

The method that adopts described power distribution cabinet gas-insulated prediction of strength device to predict, process flow diagram may further comprise the steps as shown in Figure 5:

Step 1, gather insulating coefficient, operating voltage, dropout current and four parameters of air pressure of power distribution cabinet, and above-mentioned four parameters are sent in the data acquisition chip;

Step 2, data acquisition chip carry out analog to digital conversion to four parameters that gather, and four parameters after the analog to digital conversion are sent in the data processor;

Step 3, data processor are predicted power distribution cabinet gas-insulated intensity according to five parameters that gather;

Step 3-1, employing empirical mode decomposition method (empirical mode decomposition, EMD) are carried out resolution process to the time series that insulating coefficient, operating voltage, dropout current and four parameters of air pressure by power distribution cabinet consist of;

Gather insulating coefficient, operating voltage, dropout current and the air pressure of power distribution cabinet as input quantity

Z (t)=[z ₁(t), z ₂(t) ..., z ₄(t)]; Wherein, z ₁The data of the 1st input quantity in four parameters that expression t gathers constantly;

z ₂(t) data of the 2nd input quantity in four parameters constantly gathering of expression t; z ₄(t) data of the 4th input quantity in four parameters constantly gathering of expression t;

In the embodiment of the invention, the collecting sample value sees Table 1;

Table 1

Collecting sample	The collection value
		Insulating coefficient	1.02
Operating voltage	38kv
		Dropout current	35kA
Air pressure	1005kpa

In the embodiment of the invention, adopt empirical mode decomposition method EMD method with time series z (t)=[z ₁(t), z ₂(t) ..., z ₄(t)] normalize between [0,1], and then decompose this sequence, become limited basic model component

N wherein _tBe natural number; Before to each basic model component prediction, still need each component is carried out normalized, to improve precision of prediction.

Adopt the empirical mode decomposition method to carry out resolution process, specific as follows:

Step 3-1-1, determine the Local Extremum that time series signal is all, and adopt the cubic spline interpolation curve that all Local modulus maximas are coupled together to form the coenvelope line;

Step 3-1-2, adopt the cubic spline interpolation curve that all local minizing points are coupled together to form the lower envelope line, determine all data points of envelope envelope according to envelope up and down;

Step 3-1-3, the mean value of envelope is designated as m up and down ₁, obtain difference h ₁:

h ₁＝z(t)-m ₁ (1)

If h ₁Maximal value h _MaxWith minimum value h _MinDifference less than a predefined value ε, ε≤0.01

|h _max-h _min|≤ε (2)

H then ₁A mode component for z (t); Otherwise execution in step 3-1-4;

If step 3-1-4 is h ₁Do not satisfy the condition of formula (2), then with h ₁As raw data, repeating step 3-1-1 obtains the up and down mean value m of envelope to step 3-1-3 ₂, judge again h ₂=h ₁-m ₂Whether satisfy the condition of (2) formula, if do not satisfy, then recirculate k time, until h _k=h _K-1-m _kSo that h _k(t) satisfy the condition of formula (2), note c ₁=h _k, c then ₁For first of signal z (t) satisfies the mode component of formula (2) condition;

Step 3-1-5, with c ₁After from z (t), separating, obtain z (t) remainder r ₁:

r ₁＝z(t)-c ₁ (3)

With r ₁As the process of raw data repeating step 3-1-1 to step 3-1-4, obtain the 2nd the component c that satisfies formula (2) condition of z (t) ₂, n recirculates _tInferior, obtain the n of signal z (t) _tThe individual component that satisfies formula (2) condition is worked as r ₁When becoming a monotonic quantity and can not therefrom extract the component that satisfies formula (2) condition again, circulation finishes, and any one signal z (t) is decomposed into n _tIndividual mode component With a residual

Sum, that is:

$z\left(t\right)=\underset{i_1=1}{\overset{n_{\mathrm{<figure-callout\; id="1"\; label="t\; c\; i"\; filenames="HDA00003389249300021.png,HDA00003389249300032.png"\; state="\{\{state\}\}">t</figure-callout>}}}{\mathrm{\&Sigma;}}}{c}_{i_{}}{{}_1}_{}+r_{n_t}---\left(4\right)$

Wherein, component Represent the characteristic signal of the different time that comprises in the original signal, established x (t), and order

X (t) has represented the central tendency of signal z (t).

Step 3-2, employing recurrent neural network method are predicted decomposing rear time series, and based on Phase-space Reconstruction, time series after decomposing is embedded in the phase space, employing mutual information method is determined the time delay in the phase space, adopt embedding dimension algorithm to determine the correlation dimension of phase space, and then obtain a plurality of different anticipation components;

In the embodiment of the invention, carry out the Dynamical Recurrent Neural Networks chaos analysis, through Chaotic Time Series Analysis, the evolution of arbitrary component is determined by interactional other component with it in the system, implying the full detail of system in the evolutionary process of each component, unique observable be single argument One-dimension Time Series x (t), D is dimension of attractor, and One-dimension Time Series is embedded in the m-dimensional space:

X(t)＝{x(t)，x(t-τ)，...，x(t-(m-1)τ)} (5)

Wherein, the dynamics state of etching system during X (t) expression t, τ is time delay, τ=1,2 ..., n ₁, n ₁Be natural number; M is the dimension of embedded space, thereby can set up phase space R ^mTo embedded space R ^mMapping, in the situation that m should satisfy when having the noise phase space reconstruction: m 〉=2D+1,

Step 3-3-1, employing mutual information method are determined delay time T;

The mutual information method be the time-delay that reaches for the first time hour with mutual information as the time delay of phase space reconfiguration, represent the uncertainty degree of discrete random variable x with the closely related H of information (x),

$H\left(x\right)=-\underset{i}{\mathrm{\&Sigma;}}P\left(x_i\left(t\right)\right)\log P\left(x_i\left(t\right)\right)---\left(6\right)$

Wherein, x _i(t) i data volume in the expression x (t), i=1,2 ... 4; P (x _i(t)) be generation event x _i(t) probability is for two different stochastic variable X and Y, wherein

X＝{x ₁(t)，x ₂(t)，...，x ₄(t)}，Y＝{y ₁(t)，y ₂(t)，...，y ₄(t)}，y _j(t)＝x(t+jτ)，j＝1，2，...4；

Definable X to the closely related H of the conditional information of Y (X|Y) is:

$H\left(X\right|Y)=-\underset{i,j}{\mathrm{\&Sigma;}}P\left(y_j\left(t\right)\right)P\left(x_i\left(t\right)\right|y_j\left(t\right))\log P\left(x_i\left(t\right)\right|y_j\left(t\right))---\left(7\right)$

Wherein, P (y _j(t)) be the probability of independent generation event, P (x _i(t) | y _j(t)) for being y in the generation event _j(t) event x under the condition _i(t) conditional probability that occurs, for whole variable X, because the generation of variable Y and the correlativity of the two make its uncertain moisture in the soil value that reduces be called mutual information moisture in the soil I (X, Y):

$I(X,Y)=\underset{x_i\left(t\right),y_i\left(t\right)}{\mathrm{\&Sigma;}}P(x_i\left(t\right),y_j\left(t\right))\ln \frac{P(x_i\left(t\right),y_j\left(t\right))}{P\left(x_i\left(t\right)\right)P\left(y_j\left(t\right)\right)}---\left(8\right)$

Wherein, P (x _i(t), y _j(t)) expression x _i(t) event and y _j(t) the simultaneous probability of event;

Mutual information is expressed as the function I (τ) of delay time T, can gets:

$I\left(\mathrm{\&tau;}\right)=\underset{i=1\&RightArrow;5}{\mathrm{\&Sigma;}}P(x_i\left(t\right),x_{i+\mathrm{\&tau;}}\left(t\right))\ln \frac{P(x_i\left(t\right),x_{i+\mathrm{\&tau;}}\left(t\right))}{P\left(x_i\left(t\right)\right)P\left(x_{i+\mathrm{\&tau;}}\left(t\right)\right)}---\left(9\right)$

Wherein, x _{I+ τ}(t) expression x _i(t) postpone the value of τ after the time;

The τ of I (τ) when minimal value occurring first is time delay:

$\mathrm{\&tau;}=\min \left(\underset{x_i,y_i}{\mathrm{\&Sigma;}}P(x_i\left(t\right),y_j\left(t\right))\ln \frac{P(x_i\left(t\right),y_j\left(t\right))}{P\left(x_i\left(t\right)\right)P\left(x_j\left(t\right)\right)}\right)---\left(10\right)$

Step 3-2-2, employing embed the dimension algorithm and determine correlation dimension m;

According to the algorithm that embeds dimension, optional fixed reference point X from phase space M point _{I '}, calculate all the other M-1 points to X _{I '}Distance:

$v_{i^{\&prime;}{j}^{\&prime;}}={\left[\mathrm{\&Sigma;}{(X_{i^{\&prime;}}-X_{j^{\&prime;}})}^2\right]}^{\frac{1}{2}},i^{\&prime;}=\mathrm{1,2},...,M;j^{\&prime;}=\mathrm{1,2},...,M---\left(11\right)$

Wherein, M is natural number; Repeat this process, obtain correlation integral, an any given distance value v, v=0.3 in the embodiment of the invention, checking has what to point (X _{I '}, X _{J '}) between distance less than v, distance less than v to point all to point in shared proportion be that to represent to have what state points be to be mutually related to correlation function, it is the dense degree of state point in the phase space, thereby also reflected correlation degree and the characteristics of motion degree of system motion, the relation between D and the v satisfies following formula:

$\underset{m\&RightArrow;\&infin;}{\lim }\frac{1}{m}\underset{i^{\&prime;},j^{\&prime;}=1}{\overset{m}{\mathrm{\&Sigma;}}}{H}_{\mathrm{\&theta;}}(v-v_{i^{\&prime;}{j}^{\&prime;}})={3v}^D---\left(12\right)$

Wherein, H _θ() is the Heaviside function; Then can be got by formula (11):

$D={\log }_v^{\frac{\underset{m\&RightArrow;\&infin;}{\lim }\frac{1}{m}\underset{i^{\&prime;},j^{\&prime;}=1}{\overset{m}{\mathrm{\&Sigma;}}}{H}_{\mathrm{\&theta;}}(v-v_{i^{\&prime;}{j}^{\&prime;}})}{3}}---\left(13\right)$

Then can obtain m=2D+1.

Certainly exist function F () in the phase space so that the state after the time-delay

And satisfy between the current state X (t):

$\hat{x}(t+\mathrm{\&tau;})=F\left(X\left(t\right)\right)---\left(14\right)$

F () namely is anticipation function to be sought.

Time series X (t) is a limited discrete series, carries out the chaos time sequence multi-step prediction, uses interpolation method that sequence is expanded, and then carries out the analysis of chaotic parameter, establishes η=1,2 ..., n ₂, n ₂Be natural number; At R ^mExistence function in the space, satisfy:

T '=t/ η wherein, η τ=p is the prediction step number;

In the embodiment of the invention, use the interpolation sequence spreading, obtain sequence:

X′(t′)＝{x′(t′)|t′＝1，2，...，n′} (15)

It is carried out the sequence interpolation, and λ is the interpolation multiple, has inserted the value of one times of quantity in λ=1 expression sequence, and original time series X (t) becomes X ' (t '), obtains a plurality of different anticipation components:

$\hat{x}(t^{\&prime;}+(\mathrm{\&lambda;}+1)p)=F(x^{\&prime;}\left(t^{\&prime;}\right),x^{\&prime;}(t^{\&prime;}-\mathrm{\&tau;}),...,x^{\&prime;}(t^{\&prime;}-(m-1)\mathrm{\&tau;}))---\left(16\right)$

Step 3-3, Dynamical Recurrent Neural Networks structure (Dynamical Recurrent Neural Networks is set, DRNN), the a plurality of different anticipation component input Dynamical Recurrent Neural Networks that step 3-2 obtains is trained, and adopt the method for linear combination that all the different components after training are carried out linear combination, namely obtain the health index anticipation function of power distribution cabinet

Neural network is simplified to the Dynamical Recurrent Neural Networks that three-layer network is used for direct multi-step prediction as shown in Figure 6, wherein 1 expression recurrence connection, 2 is the feedforward connection, wherein contain an input layer as cushion, a non-linear implicit layer and a linear output layer;

If being input as of network: X ' (t ')=x (t '), x (t '-τ) ..., x (t '-(m-1) τ) }, be output as G (t+p) namely

In Dynamical Recurrent Neural Networks, the i of l layer the general matching formula of neuronic input and output is:

$\left\{\begin{array}{c}{s}_{i^{\&prime;\&prime;}}^{\left[l\right]}(t+p)=\underset{i^{\&prime;\&prime;},j^{\&prime;\&prime;}=1}{\overset{N^{\left[l\right]}}{\mathrm{\&Sigma;}}}{w}_{j^{\&prime;\&prime;}}^{\left[l\right]}{w}_{i^{\&prime;\&prime;}}^{\left[l\right]}{o}_{i^{\&prime;\&prime;}}^{\left[l\right]}\left(t\right)+\underset{i^{\&prime;\&prime;},j^{\&prime;\&prime;}=1}{\overset{N^{[l-1]}}{\mathrm{\&Sigma;}}}{w}_{[j^{\&prime;\&prime;},i^{\&prime;\&prime;}]}^{[l-1,l]}{o}_{j^{\&prime;\&prime;}}^{[l-1]}\left(t\right)b_{i^{\&prime;\&prime;}}^{\left[l\right]}\\ o_{i^{\&prime;\&prime;}}^{\left[l\right]}(t+p)={\mathrm{\&theta;}}_{i^{\&prime;\&prime;}}^{\left[l\right]}\left(s_{i^{\&prime;\&prime;}}^{\left[l\right]}(t+p)\right)\end{array}\right.---\left(17\right)$

Wherein,

With

Represent respectively i " individual neuronic state and the output of l layer;

Represent neuronic deviation;

Expression is linked at the individual interneuronal weight of j " i of individual neuron and l-1 layer " of l layer; , N ^[l]It is the neuronic number of l layer;

Activation function,

The j that represents the l layer " individual neuronic weight; The i that represents the l layer " individual neuronic weight;

The j that represents the l-1 layer " individual neuronic output; I "=1...N ^[l]J "=1...N ^[l]

In the embodiment of the invention, the input layer number of network is m, the last of hidden layer determined and can selected different neuron numbers, relatively determine on the basis of its performance index size, the output layer nodes is the output variable number of unidentified system, and the Recursive Neural Network Structure model that is used for direct multi-step prediction is:

$\left\{\begin{array}{c}S(t^{\&prime;}+(\mathrm{\&lambda;}+1)p)=\mathrm{\&Theta;}(W^{[1\&RightArrow;2]}{s}_1^{\left[1\right]}\left(t^{\&prime;}\right)+W^{[1\&RightArrow;2]}{x}^{\&prime;}\left(t^{\&prime;}\right)+b^{\left[2\right]})\\ \hat{x}(t^{\&prime;}+(\mathrm{\&lambda;}+1)p)=(W^{[2\&RightArrow;3]}S(t^{\&prime;}+(\mathrm{\&lambda;}+1)p)+b^{\left[3\right]})\end{array}\right.---\left(18\right)$

Wherein, N of Θ () ^[2]The vector set of dimension, S (t '+(λ+1) p) be the state set of hidden neuron;

$b^{\left[2\right]}=[b_1^{\left[2\right]},b_2^{\left[2\right]},...,b_N^{\left[2\right]}];$

$\left\{\begin{array}{c}{W}^{[1\&RightArrow;2]}={\left[w_{[i^{\&prime;\&prime;},j^{\&prime;\&prime;}]}^{\left[\mathrm{1,2}\right]}\right|i^{\&prime;\&prime;}=\mathrm{1,2},...,N^{\left[1\right]};j^{\&prime;\&prime;}=\mathrm{1,2},...,N^{\left[2\right]}]}^T\\ W^{[2\&RightArrow;2]}={\left[w_{[i^{\&prime;\&prime;},j^{\&prime;}]}^{\left[\mathrm{2,2}\right]}\right|i^{\&prime;\&prime;}=\mathrm{1,2},...,N^{\left[2\right]};j^{\&prime;\&prime;}=\mathrm{1,2},...,N^{\left[2\right]}]}^T\\ W^{[2\&RightArrow;3]}={\left[W_{[i,j]}^{\left[\mathrm{2,3}\right]}\right|i=\mathrm{1,2},...,N^{\left[2\right]};j=\mathrm{1,2},...,N^{\left[3\right]}]}^T\end{array}\right.;$

The state set of hidden neuron is expressed as:

$S(t^{\&prime;}+(\mathrm{\&lambda;}+1)p)={[s_1^{\left[2\right]}\left(t^{\&prime;}(\mathrm{\&lambda;}+1)p\right),s_2^{\left[2\right]}\left(t^{\&prime;}(\mathrm{\&lambda;}+1)p\right),...,s_{N^{\left[l\right]}}^{\left[2\right]}\left(t^{\&prime;}(\mathrm{\&lambda;}+1)p\right)]}^T---\left(19\right)$

With predicting the outcome of all different components

Be grouped together linearly to predict the next sample point of original time series

$\hat{x}(t^{\&prime;}+p)=\underset{k^{\&prime;}=1}{\overset{n_t+1}{\mathrm{\&Sigma;}}}{\mathrm{<figure-callout\; id="1"\; label="w\; j"\; filenames="HDA00003389249300021.png,HDA00003389249300032.png"\; state="\{\{state\}\}">w</figure-callout>}}_{j_{}}^{{}_1}{\hat{x}}_{k^{\&prime;}}(t^{\&prime;}+p)---\left(20\right)$

The input layer number equals the number of multilayer neural network, and network does not have hidden layer, and a linearity output unit is arranged, and is used for determining in final predicting the outcome the weights that each component is corresponding

j ₁=1 ..., J, J are natural number, can calculate by the contrary method of Moore-Penrose of asking formula (20).

With

As power distribution cabinet gas-insulated strength function, the time series that generates take collection capacity calculates power distribution cabinet gas-insulated prediction of strength result as input;

Figure 7 shows that empirical mode and Neural network mixed model structure, in the phase one,

Be the Mode Decomposition unit; I is interpolating unit, in the subordinate phase

Be DRNN structural design unit, in the phase III, It is Dynamical Recurrent Neural Networks.

Step 4, data processor are sent to the power distribution cabinet gas-insulated intensity of predicting in the industrial computer and store, and are sent to the remote dispatching terminal by wireless communication module, so that the maintenance personal in time overhauls.Power distribution cabinet gas-insulated Model To Describe Strength of Blended predict the outcome with measured value more as shown in Figure 8, in the power distribution cabinet gas-insulated Model To Describe Strength of Blended of setting up, the error of power distribution cabinet gas-insulated prediction of strength is controlled at basically ± 6% within.

Claims (4)

1. a power distribution cabinet gas-insulated prediction of strength device is characterized in that: comprise current sensor, voltage sensor, baroceptor, insulation tester, data acquisition chip, central processing unit, industrial computer and wireless communication module; Wherein, one road input end of the output terminal connection data acquisition chip of described current sensor; Another road input end of the output terminal connection data acquisition chip of voltage sensor; The another road input end of the output terminal connection data acquisition chip of baroceptor; The another road input end of the output terminal connection data acquisition chip of insulation tester; The output terminal of data acquisition chip connects the input end of central processing unit, and the two-way output terminal of central processing unit connects respectively the input end of industrial computer and the input end of wireless communication module.

2. the method that adopts power distribution cabinet gas-insulated prediction of strength device claimed in claim 1 to predict is characterized in that: may further comprise the steps:

Step 1, gather insulating coefficient, operating voltage, dropout current and four parameters of air pressure of power distribution cabinet, and above-mentioned four parameters are sent in the data acquisition chip;

Step 2, data acquisition chip carry out analog to digital conversion to four parameters that gather, and four parameters after the analog to digital conversion are sent in the data processor;

Step 3, data processor are predicted power distribution cabinet gas-insulated intensity according to four parameters that gather;

Step 3-1, employing empirical mode decomposition method are carried out resolution process to the time series that insulating coefficient, operating voltage, dropout current and four parameters of air pressure by power distribution cabinet consist of;

Step 3-2, employing recurrent neural network method are predicted decomposing rear time series, and based on Phase-space Reconstruction, time series after decomposing is embedded in the phase space, employing mutual information method is determined the time delay in the phase space, adopt embedding dimension algorithm to determine the correlation dimension of phase space, and then obtain a plurality of different anticipation components;

Step 3-3, the Dynamical Recurrent Neural Networks structure is set, the a plurality of different anticipation component input Dynamical Recurrent Neural Networks that step 3-2 obtains is trained, and adopt the method for linear combination that all the different components after training are carried out linear combination, namely obtain power distribution cabinet gas-insulated prediction of strength function;

Step 4, data processor are sent to the power distribution cabinet gas-insulated intensity of predicting in the industrial computer and store, and are sent to the remote dispatching terminal by wireless communication module, so that the maintenance personal in time overhauls.

3. the method predicted of employing power distribution cabinet gas-insulated prediction of strength device according to claim 2, it is characterized in that: the described employing empirical mode decomposition of step 3-1 method is carried out resolution process to the time series that insulating coefficient, operating voltage, dropout current and four parameters of air pressure by power distribution cabinet consist of, and specifically may further comprise the steps:

Step 3-1-1, determine the Local Extremum that time series signal is all, and adopt the cubic spline interpolation curve that all Local modulus maximas are coupled together to form the coenvelope line;

Step 3-1-2, adopt the cubic spline interpolation curve that all local minizing points are coupled together to form the lower envelope line, determine all data points of envelope envelope according to envelope up and down;

Step 3-1-3, the mean value of envelope is designated as m up and down ₁, obtain difference h ₁:

h ₁＝z(t)-m ₁ (1)

Wherein, z (t)=[z ₁(t), z ₂(t) ..., z ₄(t)]; z ₁(t) data of the 1st input quantity in four parameters constantly gathering of expression t; z ₂(t) data of the 2nd input quantity in four parameters constantly gathering of expression t; z ₄(t) data of the 4th input quantity in four parameters constantly gathering of expression t;

If h ₁Maximal value h _MaxWith minimum value h _MinDifference less than a predefined value ε, ε≤0.01

|h _max-h _min|≤ε (2)

H then ₁A mode component for z (t); Otherwise, execution in step 3-1-4;

Step 3-1-4, with h ₁As another primary data, repeating step 3-1-1 recirculates k time, until h to step 3-1-3 _k=h _K-1-m _kMiddle h _kSatisfy the condition of formula (2), note c ₁h _k, c then ₁First mode component for signal z (t);

Step 3-1-5, with c ₁From z (t), separate, obtain z (t) remainder r ₁:

r ₁＝z(t)-c ₁ (3)

With r ₁To step 3-1-4, n recirculates as another primary data repeating step 3-1-1 _tInferior, obtain the n of signal z (t) _tIndividual mode component;

When the remainder of z (t) was a monotonic quantity, circulation finished, and signal z (t) is decomposed into n _tIndividual mode component With a residual

Sum, that is:

$z\left(t\right)=\underset{i_1=1}{\overset{n_t}{\mathrm{\&Sigma;}}}{c}_{i_1}+r_{n_t}---\left(4\right)$

Wherein, component

Represent the characteristic signal of the different time that comprises among the original signal z (t), established x (t), and order X (t) has represented the central tendency of signal z (t).

4. the method predicted of employing power distribution cabinet gas-insulated prediction of strength device according to claim 2, it is characterized in that: step 3-2 is described based on Phase-space Reconstruction, time series after decomposing is embedded in the phase space, time series x (t) after being about to decompose is embedded into phase space X (t)={ x (t), x (t-τ), ..., x (t-(m-1) τ) } in, line number is the number of parameters that gathers in the described phase space, and columns is correlation dimension;

Wherein, the dynamics state of etching system during X (t) expression t, τ is time delay, τ=1,2 ..., n ₁, n ₁Be natural number; M is the dimension of embedded space;

Described employing recurrent neural network method is predicted decomposing rear time series, determines and certainly exists function F () in the phase space so that the time series state after the time-delay

And satisfy between the current state X (t):

$\hat{x}(t+\mathrm{\&tau;})=F\left(X\left(t\right)\right)---\left(14\right)$

F () is power distribution cabinet gas-insulated prediction of strength function to be sought;

And adopt interpolation method that sequence is expanded, obtain a plurality of different anticipation components:

$\hat{x}(t^{\&prime;}+(\mathrm{\&lambda;}+1)p)=F(x^{\&prime;}\left(t^{\&prime;}\right),x^{\&prime;}(t^{\&prime;}-\mathrm{\&tau;}),...,x^{\&prime;}(t^{\&prime;}-(m-1)\mathrm{\&tau;}))---\left(16\right)$

Wherein, λ is the interpolation multiple, t ' is the moment after the interpolation, p is the prediction step number, x ' (t ') is the time series after the interpolation, x ' (t '-τ) be the time series that postpones the τ time after the interpolation, x ' (t '-(m-1) τ) be the time series of delay (m-1) τ time after the interpolation.

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