CN105203153B - 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 meanss and Forecasting Methodology

Technical field

The invention belongs to power failure electric powder prediction, more particularly to a kind of power consumer significant trouble risk index is pre- Survey device and Forecasting Methodology.

Background technology

Power consumer in power system, occur significant trouble when, extensive load loss can be brought to power system with And larger grid stability impacts, user capacity is bigger, impact and lose bigger that its failure and power failure are caused to electrical network, Therefore, real-time monitoring is carried out to the electric parameter and environment parament at power consumer critical point, and according to monitoring parameter to electric power User's significant trouble risk is predicted, and electric network emergency measure is arranged according to predicting the outcome, and can be prevented effectively from electric power Impact and impact that user's significant trouble brings on electrical network, significantly improve Power System Reliability and economy.

The content of the invention

The present invention is aiming at the problems referred to above, there is provided a kind of degree of accuracy is high, the power consumer significant trouble that predictive efficiency is high Risk index prediction meanss and Forecasting Methodology.

For achieving the above object, the present invention adopts the following technical scheme that the present invention includes that signal gathering unit, A/D are changed Unit, CPU, 4G communications unit and human-machine interactive information display unit, its structural feature signal gathering unit Output port be connected with the input port of A/D converting units, the output port of A/D converting units and the number of CPU It is connected according to input port, the information output port of CPU information input port respectively with 4G communications units, The information input port of human-machine interactive information display unit is connected.

Used as a kind of preferred version, signal gathering unit of the present invention includes voltage sensor, current sensor, rainfall Quantity sensor, vibrating sensor, 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 respectively with The input port of A/D converting units is connected.

Used as another kind of preferred version, voltage sensor of the present invention adopts DH51D6V0.4B models, current sensor Using DHC03B models, rainfall amount sensor adopts BL～YW900 model radar level gauges, vibrating sensor to adopt STA9200A Model, baroceptor adopts LC～QA1 models.

Secondly, A/D converting units of the present invention adopt TLC2543 serial a/d transducers, CPU to adopt type Number for STC89C51 single-chip microcomputer, 4G communications unit using ME3760 models LTE module, human-machine interactive information shows single Unit adopts the LCD MODULE of HG1286402C models；

Voltage sensor signals output port, current sensor signal output port, rainfall amount sensor signal output part Mouth, vibration sensor signal output port, baroceptor signal output port connect respectively through correspondence after signaling conversion circuit Be connected to the input AIN0～AIN4 of A/D converter TLC2543, the outfan EOC of A/D converter TLC2543, I/O, IN, OUT, CS are connected respectively the P0.0-P0.4 pins of single-chip microcomputer STC89C51 chips, single-chip microcomputer STC89C51 chips P1.0～P1.7 connections corresponding with the D0～D7 of LCD MODULE, the P2.0～P1.4 and liquid crystal of single-chip microcomputer STC89C51 chips The corresponding connection of RS, RW, CS1, CS2, EN of display module, RXD, TXD pin and 4G communication modules of STC89C51 chips DATA, DATA1 end correspondence of ME3760 is connected, and the ATN1 ends of 4G communication modules are connected with antenna.

In addition, signaling conversion circuit of the present invention adopts TLC4501 chips.(signaling conversion circuit is set, it is ensured that signal The bandwidth of collection, switching rate and voltage gain, while reducing input offset voltage and electric current and temperature drift)

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

Step 1：Voltage, electric current, rainfall, vibration number, the ambient atmosphere pressure parameter of collection power consumer, electric power is used Voltage, electric current, rainfall, vibration number, the ambient atmosphere pressure at family is used as input quantity；

Step 2：Set up reliability model

Reliability Rs (x) model is：

Fault rate λ_iFor constant 0.13,

Step 4：Calculate the upper lower limit value of reliability

N=5, the reliability upper limit is：

Its lower limit model is：

Step 5：Calculating judges that stop condition is：

In formula, ε is equal to 0.02.(ε is computational accuracy, and Jing long term tests take 0.02 and reach good prediction effect)

Step 6：Calculate and match somebody with somebody power consumer significant trouble risk index value：He=R (x)

Step 7：Will be with power consumer significant trouble risk index value：Predicting the outcome for He=R (x) is aobvious by Liquid Crystal Module Show and afield dispatch terminal is sent by 4G transport modules, so that maintainer is overhauled in time.

Beneficial effect of the present invention.

The present invention by the use of the voltage of direct measurement power consumer, electric current, rainfall, vibration number, ambient atmosphere pressure as Input quantity, and finally utilize A/D converting units, CPU CPU, human-machine interactive information display unit and 4G transport modules Realize the monitoring of power consumer material risk index.This method avoids what traditional method was caused when setting up model and Selecting All Parameters Error, and extract simple with input quantity, degree of accuracy is high, and accuracy is good, the characteristics of predictive efficiency is high.

Power consumer significant trouble risk is made prediction, electrical network major accident can be prevented, improve power quality, improved Electricity consumption reliability, while prediction process meets requirement of real-time, improves the efficiency of data acquisition and process, improves power consumer weight The speed and precision of major break down risk profile, is realized power distribution network electric power is used with degree of precision and compared with the advantage of short response time The significant trouble at family is predicted.

Description of the drawings

With reference to the accompanying drawings and detailed description the present invention will be further described.The scope of the present invention not only limits to In the statement of herein below.

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

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

Specific embodiment

As illustrated, power consumer significant trouble risk index Forecasting Methodology of the present invention, comprises the steps, including it is as follows Step：

Step 1：Voltage, electric current, rainfall, vibration number, the ambient atmosphere pressure parameter of collection power consumer, electric power is used Voltage, electric current, rainfall, vibration number, the ambient atmosphere pressure at family is used as input quantity：S=(10.98,339.12,10.13, 21.90,1.00,3.57)；

Step 2：Set up reliability model

When all subsystems in the production system being made up of n subsystem, only system all break down, whole system Ability failure.Such system belongs to parallel system.

Reliability R_sX () model is：

Fault rate λ_iFor constant 0.13,

Parallel system, subsystem quantity directly affects system dependability.

Step 4：Calculate the upper lower limit value of reliability

N=5, the reliability upper limit is：

N be subsystem number, q_iTo cause the fault rate of the subsystem of the system failure；V is the quantity of this subsystem.System System Lower Confidence Limit R_lIt is exactly various shape probability of state sums that system can normally be run.

Its lower limit model is：

Step 5：Calculating judges that stop condition is：

In formula, ε is equal to 0.02.

Step 6：Calculate and match somebody with somebody power consumer significant trouble risk index value：He=R (x)

Step 7：Will be with power consumer significant trouble risk index value：Predicting the outcome for He=R (x) is aobvious by Liquid Crystal Module Show and afield dispatch terminal is sent by 4G transport modules, so that maintainer is overhauled in time.

It is understood that above with respect to the specific descriptions of the present invention, being merely to illustrate the present invention and being not limited to this Technical scheme described by inventive embodiments, it will be understood by those within the art that, still the present invention can be carried out Modification or equivalent, to reach identical technique effect；As long as meet use needs, all protection scope of the present invention it It is interior.

Claims (1)

1. a kind of power consumer significant trouble risk index Forecasting Methodology, it is characterised in that comprise the steps：

Step 1：Voltage, electric current, rainfall, vibration number, the ambient atmosphere pressure parameter of collection power consumer, by power consumer Voltage, electric current, rainfall, vibration number, ambient atmosphere pressure are used as input quantity；

Step 2：Set up reliability model

Reliability R_sX () model is：

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

Fault rate λ_iFor constant 0.13,

Step 3：Calculate the upper lower limit value of reliability

N=5, the reliability upper limit is：

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

Its lower limit model is：

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

Step 4：Calculating judges that stop condition is：

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

In formula, ε is equal to 0.02；

Step 5：Calculate and match somebody with somebody power consumer significant trouble risk index value：He=R (x)

Step 6：Will be with power consumer significant trouble risk index value：Predicting the outcome for He=R (x) is shown simultaneously by Liquid Crystal Module Afield dispatch terminal is sent by 4G transport modules, so that maintainer is overhauled in time.