CN105203153A - Electric power user major fault risk index prediction device and prediction method - Google Patents

Abstract

The invention relates to an electric power user major fault risk index prediction device and prediction method and belongs to the electric power fault prediction technical field. The electric power user major fault risk index prediction device and prediction method provided by the invention have the advantages of high accuracy and high prediction efficiency. The prediction device of the invention comprises a signal acquisition unit, an A/D conversion unit, a central processing unit, a 4G communication transmission unit and a man-machine interaction information display unit. The prediction device is structurally characterized in that the output port of the signal acquisition unit is connected with the input port of the A/D conversion unit; the output port of the A/D conversion unit is connected with the data input port of the central processing unit; the information output ports of the central processing unit are connected with the information input port of the 4G communication transmission unit and the information input port of the man-machine interaction information display unit respectively.

Description

A kind of power consumer significant trouble risk index prediction unit and Forecasting Methodology

Technical field

The invention belongs to power failure electric powder prediction, particularly relate to a kind of power consumer significant trouble risk index prediction unit and Forecasting Methodology.

Background technology

Power consumer in electric system, when there is significant trouble, extensive load loss and larger grid stability can be brought to impact to electric system, user capacity is larger, the impact that its fault and having a power failure causes electrical network and loss larger, therefore, Real-Time Monitoring is carried out to the electric parameter at power consumer critical point and environment parament, and according to monitoring parameter, power consumer significant trouble risk is predicted, according to predicting the outcome electric network emergency measure arranged, the impact can effectively avoiding power consumer significant trouble to bring electrical network and impact, significantly improve Power System Reliability and economy.

Summary of the invention

The present invention is exactly for the problems referred to above, provides a kind of degree of accuracy is high, forecasting efficiency is high power consumer significant trouble risk index prediction unit and Forecasting Methodology.

For achieving the above object, the present invention adopts following technical scheme, the present invention includes signal gathering unit, A/D converting unit, CPU (central processing unit), 4G communications unit and human-machine interactive information display unit, the output port of its structural feature signal gathering unit is connected with the input port of A/D converting unit, the output port of A/D converting unit is connected with the data-in port of CPU (central processing unit), and the information output mouth of CPU (central processing unit) is connected with the information input terminal mouth of 4G communications unit, the information input terminal mouth of human-machine interactive information display unit respectively.

As a kind of preferred version, signal gathering unit of the present invention comprises voltage sensor, current sensor, rainfall amount sensor, vibration transducer, baroceptor, and voltage sensor signals output port, current sensor signal output port, rainfall amount sensor signal output port, vibration sensor signal output port, baroceptor signal output port are connected with the input port of A/D converting unit respectively.

As another kind of preferred version, voltage sensor of the present invention adopts DH51D6V0.4B model, and current sensor adopts DHC03B model, and rainfall amount sensor adopts BL ~ YW900 model radar level gauge, vibration transducer adopts STA9200A model, and baroceptor adopts LC ~ QA1 model.

Secondly, A/D converting unit of the present invention adopts TLC2543 serial a/d converter, CPU (central processing unit) adopts model to be the single-chip microcomputer of STC89C51,4G communications unit adopts the LTE module of ME3760 model, and human-machine interactive information display unit adopts the LCD MODULE of HG1286402C model;

Voltage sensor signals output port, current sensor signal output port, rainfall amount sensor signal output port, vibration sensor signal output port, baroceptor signal output port is respectively through the input end AIN0 ~ AIN4 being connected to A/D converter TLC2543 corresponding after signaling conversion circuit, the output terminal EOC of A/D converter TLC2543, I/O, IN, OUT, CS is connected respectively the P0.0-P0.4 pin of single-chip microcomputer STC89C51 chip, P1.0 ~ the P1.7 of single-chip microcomputer STC89C51 chip is corresponding with the D0 ~ D7 of LCD MODULE to be connected, P2.0 ~ the P1.4 of single-chip microcomputer STC89C51 chip and the RS of LCD MODULE, RW, CS1, CS2, the corresponding connection of EN, the RXD of STC89C51 chip, the DATA of TXD pin and 4G communication module ME3760, DATA1 end is corresponding to be connected, the ATN1 end of 4G communication module is connected with antenna.

In addition, signaling conversion circuit of the present invention adopts TLC4501 chip.(signalization change-over circuit, ensures the frequency span of signals collecting, switching rate and voltage gain, reduces input offset voltage and electric current and temperature drift simultaneously)

A kind of power consumer significant trouble risk index Forecasting Methodology, comprises the steps:

Step 1: the voltage, electric current, rainfall amount, vibration number, the ambient atmosphere pressure parameter that gather power consumer, using the voltage of power consumer, electric current, rainfall amount, vibration number, ambient atmosphere pressure as input quantity;

Step 2: set up reliability model

Fiduciary level Rs (x) model is:

$R_s\left(x\right)=1-\underset{i=1}{\overset{n}{\mathrm{\Π}}}[1-e-{\mathrm{\λ}}_{i}x]---\left(1\right)$

Failure rate λ _ifor constant 0.13,

Step 4: the upper lower limit value calculating fiduciary level

N=5, the fiduciary level upper limit is:

$R_u\left(x\right)=\underset{i=1}{\overset{5}{\mathrm{\Π}}}{R}_i\left(x\right)[1-\underset{i=1}{\overset{5}{\mathrm{\Π}}}(1-q_i\left(x\right))]---\left(2\right)$

Its lower limit model is:

$R_l\left(x\right)=\underset{j=1}{\overset{5}{\mathrm{\Π}}}{R}_j\left(x\right)\{1+\underset{j=1}{\overset{5}{\mathrm{\Σ}}}\left(\frac{q_j\left(x\right)}{R_j\left(x\right)}\right)\}---\left(3\right)$

Step 5: calculate and judge that stop condition is:

$R\left(x\right)-\frac{R_u\left(x\right)-R_l\left(x\right)}{\sqrt{(1-R_u\left(x\right))(1-R_l\left(x\right))}}<\mathrm{\&epsiv;}---\left(4\right)$

In formula, ε equals 0.02.(ε is computational accuracy, through test of long duration, gets 0.02 and reaches good prediction effect)

Step 6: calculate and join power consumer significant trouble risk index value: He=R (x)

Predicting the outcome of step 7: will join power consumer significant trouble risk index value: He=R (x) to be shown by Liquid Crystal Module and to be sent to remote dispatching terminal by 4G transport module, so that maintenance personal overhauls in time.

Beneficial effect of the present invention.

The present invention utilizes the voltage of directly measurement power consumer, electric current, rainfall amount, vibration number, ambient atmosphere pressure as input quantity, and finally to utilize A/D converting unit, CPU CPU (central processing unit), human-machine interactive information display unit and 4G transport module to realize the monitoring of power consumer material risk index.This method avoids the error caused when classic method Modling model and Selecting All Parameters, and it is simple to have input quantity extraction, and degree of accuracy is high, and accuracy is good, the feature that forecasting efficiency is high.

Power consumer significant trouble risk is made prediction, electrical network major accident can be prevented, improve power quality, improve electricity consumption reliability, forecasting process requirement of real time simultaneously, improve the efficiency of data acquisition and process, improve speed and the precision of power consumer significant trouble risk profile, achieve and predict with degree of precision with compared with the significant trouble of advantage to power distribution network power consumer of short response time.

Accompanying drawing explanation

Below in conjunction with the drawings and specific embodiments, the present invention will be further described.Scope is not only confined to the statement of following content.

Fig. 1 is schematic block circuit diagram of the present invention.

Fig. 2 is circuit theory diagrams of the present invention.

Embodiment

As shown in the figure, power consumer significant trouble risk index Forecasting Methodology of the present invention, comprises the steps, comprises the steps:

Step 1: the voltage, electric current, rainfall amount, vibration number, the ambient atmosphere pressure parameter that gather power consumer, using the voltage of power consumer, electric current, rainfall amount, vibration number, ambient atmosphere pressure as input quantity: s=(10.98,339.12,10.13,21.90,1.00,3.57);

Step 2: set up reliability model

The production system be made up of n subsystem, when only having all subsystems in system all to break down, whole system is fault.Such system belongs to parallel system.

Fiduciary level R _sx () model is:

$R_s\left(x\right)=1-\underset{i=1}{\overset{n}{\mathrm{\&Pi;}}}[1-e-{\mathrm{\&lambda;}}_{i}x]---\left(1\right)$

Failure rate λ _ifor constant 0.13,

Parallel system, the direct influential system fiduciary level of subsystem quantity.

Step 4: the upper lower limit value calculating fiduciary level

N=5, the fiduciary level upper limit is:

$R_u\left(x\right)=\underset{i=1}{\overset{5}{\mathrm{\&Pi;}}}{R}_i\left(x\right)[1-\underset{i=1}{\overset{5}{\mathrm{\&Pi;}}}(1-q_i\left(x\right))]---\left(2\right)$

N is subsystem number, q _ifor causing the failure rate of the subsystem of the system failure; V is the quantity of this subsystem.System dependability lower limit R _lbe exactly the various shape probability of state sums that system can normally be run.

Its lower limit model is:

$R_l\left(x\right)=\underset{j=1}{\overset{5}{\mathrm{\&Pi;}}}{R}_j\left(x\right)\{1+\underset{j=1}{\overset{5}{\mathrm{\&Sigma;}}}\left(\frac{q_j\left(x\right)}{R_j\left(x\right)}\right)\}---\left(3\right)$

Step 5: calculate and judge that stop condition is:

$R\left(x\right)-\frac{R_u\left(x\right)-R_l\left(x\right)}{\sqrt{(1-R_u\left(x\right))(1-R_l\left(x\right))}}<\mathrm{\&epsiv;}---\left(4\right)$

In formula, ε equals 0.02.

Step 6: calculate and join power consumer significant trouble risk index value: He=R (x)

Predicting the outcome of step 7: will join power consumer significant trouble risk index value: He=R (x) to be shown by Liquid Crystal Module and to be sent to remote dispatching terminal by 4G transport module, so that maintenance personal overhauls in time.

Be understandable that, above about specific descriptions of the present invention, the technical scheme described by the embodiment of the present invention is only not limited to for illustration of the present invention, those of ordinary skill in the art is to be understood that, still can modify to the present invention or equivalent replacement, to reach identical technique effect; Needs are used, all within protection scope of the present invention as long as meet.

Claims (6)

1. a power consumer significant trouble risk index prediction unit, comprise signal gathering unit, A/D converting unit, CPU (central processing unit), 4G communications unit and human-machine interactive information display unit, it is characterized in that the output port of signal gathering unit is connected with the input port of A/D converting unit, the output port of A/D converting unit is connected with the data-in port of CPU (central processing unit), and the information output mouth of CPU (central processing unit) is connected with the information input terminal mouth of 4G communications unit, the information input terminal mouth of human-machine interactive information display unit respectively.

2. a kind of power consumer significant trouble risk index prediction unit according to claim 1, it is characterized in that described signal gathering unit comprises voltage sensor, current sensor, rainfall amount sensor, vibration transducer, baroceptor, voltage sensor signals output port, current sensor signal output port, rainfall amount sensor signal output port, vibration sensor signal output port, baroceptor signal output port are connected with the input port of A/D converting unit respectively.

3. a kind of power consumer significant trouble risk index prediction unit according to claim 2, it is characterized in that described voltage sensor adopts DH51D6V0.4B model, current sensor adopts DHC03B model, rainfall amount sensor adopts BL ~ YW900 model radar level gauge, vibration transducer adopts STA9200A model, and baroceptor adopts LC ~ QA1 model.

4. a kind of power consumer significant trouble risk index prediction unit according to claim 3, it is characterized in that described A/D converting unit adopts TLC2543 serial a/d converter, CPU (central processing unit) adopts model to be the single-chip microcomputer of STC89C51,4G communications unit adopts the LTE module of ME3760 model, and human-machine interactive information display unit adopts the LCD MODULE of HG1286402C model;

Voltage sensor signals output port, current sensor signal output port, rainfall amount sensor signal output port, vibration sensor signal output port, baroceptor signal output port is respectively through the input end AIN0 ~ AIN4 being connected to A/D converter TLC2543 corresponding after signaling conversion circuit, the output terminal EOC of A/D converter TLC2543, I/O, IN, OUT, CS is connected respectively the P0.0-P0.4 pin of single-chip microcomputer STC89C51 chip, P1.0 ~ the P1.7 of single-chip microcomputer STC89C51 chip is corresponding with the D0 ~ D7 of LCD MODULE to be connected, P2.0 ~ the P1.4 of single-chip microcomputer STC89C51 chip and the RS of LCD MODULE, RW, CS1, CS2, the corresponding connection of EN, the RXD of STC89C51 chip, the DATA of TXD pin and 4G communication module ME3760, DATA1 end is corresponding to be connected, the ATN1 end of 4G communication module is connected with antenna.

5. a kind of power consumer significant trouble risk index prediction unit according to claim 4, is characterized in that described signaling conversion circuit adopts TLC4501 chip.

6. a power consumer significant trouble risk index Forecasting Methodology, is characterized in that comprising the steps:

Step 1: the voltage, electric current, rainfall amount, vibration number, the ambient atmosphere pressure parameter that gather power consumer, using the voltage of power consumer, electric current, rainfall amount, vibration number, ambient atmosphere pressure as input quantity;

Step 2: set up reliability model

Fiduciary level R _sx () model is:

$R_s\left(x\right)=1-\underset{i=1}{\overset{n}{\mathrm{\&Pi;}}}[1-e-{\mathrm{\&lambda;}}_{i}x]---\left(1\right)$

Failure rate λ _ifor constant 0.13,

Step 4: the upper lower limit value calculating fiduciary level

N=5, the fiduciary level upper limit is:

$R_u\left(x\right)=\underset{i=1}{\overset{5}{\mathrm{\&Pi;}}}{R}_i\left(x\right)[1-\underset{i=1}{\overset{5}{\mathrm{\&Pi;}}}(1-q_i\left(x\right))]---\left(2\right)$

Its lower limit model is:

$R_l\left(x\right)=\underset{j=1}{\overset{5}{\mathrm{\&Pi;}}}{R}_j\left(x\right)\{1+\underset{j=1}{\overset{5}{\mathrm{\&Sigma;}}}\left(\frac{q_j\left(x\right)}{R_j\left(x\right)}\right)\}---\left(3\right)$

Step 5: calculate and judge that stop condition is:

$R\left(x\right)-\frac{R_u\left(x\right)-R_l\left(x\right)}{\sqrt{(1-R_u\left(x\right))(1-R_l\left(x\right))}}<\mathrm{\&epsiv;}---\left(4\right)$

In formula, ε equals 0.02.

Step 6: calculate and join power consumer significant trouble risk index value: He=R (x)

Predicting the outcome of step 7: will join power consumer significant trouble risk index value: He=R (x) to be shown by Liquid Crystal Module and to be sent to remote dispatching terminal by 4G transport module, so that maintenance personal overhauls in time.