CN108321932B - Bus duct temperature rise monitoring system based on wireless sensor network - Google Patents

Bus duct temperature rise monitoring system based on wireless sensor network Download PDF

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CN108321932B
CN108321932B CN201810202581.5A CN201810202581A CN108321932B CN 108321932 B CN108321932 B CN 108321932B CN 201810202581 A CN201810202581 A CN 201810202581A CN 108321932 B CN108321932 B CN 108321932B
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bus duct
temperature rise
regression
matrix
management system
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CN108321932A (en
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王天荆
郭春松
马恒宝
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Zhenjiang Sicurezza Electric Co ltd
Nanjing Tech University
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Zhenjiang Sicurezza Electric Co ltd
Nanjing Tech University
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    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J13/00—Circuit arrangements for providing remote indication of network conditions, e.g. an instantaneous record of the open or closed condition of each circuitbreaker in the network; Circuit arrangements for providing remote control of switching means in a power distribution network, e.g. switching in and out of current consumers by using a pulse code signal carried by the network
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
    • G06F17/10—Complex mathematical operations
    • G06F17/18—Complex mathematical operations for evaluating statistical data, e.g. average values, frequency distributions, probability functions, regression analysis
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
    • Y02E60/00—Enabling technologies; Technologies with a potential or indirect contribution to GHG emissions mitigation
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y04—INFORMATION OR COMMUNICATION TECHNOLOGIES HAVING AN IMPACT ON OTHER TECHNOLOGY AREAS
    • Y04S—SYSTEMS INTEGRATING TECHNOLOGIES RELATED TO POWER NETWORK OPERATION, COMMUNICATION OR INFORMATION TECHNOLOGIES FOR IMPROVING THE ELECTRICAL POWER GENERATION, TRANSMISSION, DISTRIBUTION, MANAGEMENT OR USAGE, i.e. SMART GRIDS
    • Y04S40/00—Systems for electrical power generation, transmission, distribution or end-user application management characterised by the use of communication or information technologies, or communication or information technology specific aspects supporting them
    • Y04S40/12—Systems for electrical power generation, transmission, distribution or end-user application management characterised by the use of communication or information technologies, or communication or information technology specific aspects supporting them characterised by data transport means between the monitoring, controlling or managing units and monitored, controlled or operated electrical equipment
    • Y04S40/126—Systems for electrical power generation, transmission, distribution or end-user application management characterised by the use of communication or information technologies, or communication or information technology specific aspects supporting them characterised by data transport means between the monitoring, controlling or managing units and monitored, controlled or operated electrical equipment using wireless data transmission

Abstract

The invention relates to a bus duct temperature rise monitoring system based on a wireless sensor network, which comprises a plurality of bus duct branch lines arranged in parallel, a bus duct trunk line arranged at the upstream of the bus duct branch lines, a master switch, a data fusion center and a background monitoring management system, and is characterized in that: the background monitoring and management system comprises a temperature rise linear regression module, a parameter significance inspection module, a temperature rise prediction analysis module and a residual error analysis module; the temperature data of monitoring is transmitted through the self-organized sensor network technology, a linear regression model of temperature rise is established in a data fusion center, the temperature change of the bus duct is predicted, the position of a fault point is found as soon as possible, maintenance personnel can repair and inspect the circuit quickly, and potential safety hazards are eliminated.

Description

Bus duct temperature rise monitoring system based on wireless sensor network
Technical Field
The invention relates to a monitoring system, in particular to a bus duct temperature rise monitoring system based on a wireless sensor network.
Background
With the rapid development of economy and modern construction in China and the emergence of modern engineering facilities and equipment, the power consumption of various industries is increased rapidly, the power consumption load is larger and larger, and particularly, the appearance of numerous high-rise buildings and large-scale factory workshops is difficult to meet the requirement as a traditional cable. In recent years, a bus duct system is taken as a power distribution device for efficiently transmitting current, and meets the requirement of economic and reasonable wiring of higher and higher buildings and large-scale factories. However, the bus duct has a compact structure, the copper bars are tightly pressed together, the requirements on heat dissipation and insulation are high, insulation aging can be accelerated if the temperature of the bus duct rises to a certain limit, and even insulation is damaged, so that safety accidents occur. Meanwhile, the connection points of the bus ducts in the power supply system are generally hundreds of thousands of connection points, and are often related to each other, so that a great safety accident can be brought as long as one point fails. The system for monitoring the operation condition of the bus duct in real time and having the early warning function is particularly necessary. Traditional bus duct temperature rise monitoring system adopts wired mode to communicate, and the construction degree of difficulty is great, and a small amount of measuring points realize easily, nevertheless when gathering a large amount of measuring points, will meet problems such as circuit interference, wiring difficulty, installation complicacy, economic cost height.
Disclosure of Invention
In order to overcome the defects and shortcomings in the prior art, the invention provides a bus duct temperature rise monitoring system based on a wireless sensor network.
The technical scheme adopted by the invention for solving the technical problems is as follows: a bus duct temperature rise monitoring system based on a wireless sensor network comprises a plurality of bus duct branch lines arranged in parallel, a bus duct trunk line arranged at the upstream of the bus duct branch lines, a main switch, a data fusion center and a background monitoring management system, wherein the main switch is arranged between the bus duct trunk line and the data fusion center, each bus duct branch line is provided with a leakage switch, a lower computer and a sensor node, and each leakage switch is arranged between the downstream of the bus duct trunk line and each lower computer; the data fusion center is connected with the background monitoring management system and used for sending the acquired temperature information to a computer in the background monitoring management system for data analysis; the method is characterized in that: the background monitoring and management system comprises a temperature rise linear regression module, a parameter significance inspection module, a temperature rise prediction analysis module and a residual error analysis module.
Further, the temperature rise linear regression module adopts the following model to calculate the temperature rise of the bus duct
(yi,xi1,xi2,xi3),i＝1,…,n (1)
Wherein the content of the first and second substances,
yithe temperature of the bus duct rises,
xi1is the value of the current load and,
xi2is an average of a plurality of temperature values,
xi3is a humidity value;
and satisfy
yi＝β0+βxi1+β2xi2+β3xi3+i,i＝1,…,n (2)
Whereini(i-1, …, n) are regression constants which are independent of each other and each obey a normal distributioni～N(0,σ2)，
βiIs a regression coefficient;
using a matrix expression, order
Figure GDA0002744670550000031
The temperature rise linear regression is then formulated as
Y＝Xβ+ (4)
Wherein the matrix X is a temperature rise influence factor matrix, and X columns are full rank,
the matrix Y is a bus duct temperature rise matrix,
beta is a matrix of the regression coefficients,
is a regression constant matrix;
wherein the regression coefficient beta is estimated using least squares, i.e. beta is chosen such that the sum of the squares of the error terms
S(β)＝T＝(Y-Xβ)T(Y-Xβ) (5)
The minimum value is reached, and the minimum value,
because of the Hessian matrix of S (beta) at beta
Figure GDA0002744670550000041
Is a positive definite matrix, so its least squares estimation
Figure GDA0002744670550000042
I.e., the minimum point of S (β), where k and l are the abscissa and ordinate, respectively, at β, i.e., the point β (β)k，βl)。
Further, the sensor node detects the current load value of each bus duct branch line, and collects total 6 temperature values of two phases of two sides of 3 joints of the bus duct except for N phases and humidity values around the bus duct.
Further, the regression coefficient β is recalculated every interval of time, and the regression coefficient β is not changed in adjacent 2 intervals.
Further, in the parameter significance testing module, a binary hypothesis is set for formula (4)
Figure GDA0002744670550000044
I is more than or equal to 1 and less than or equal to 3, so that beta isi≠0 (8)
Constructing F test statistics
Figure GDA0002744670550000043
Wherein the content of the first and second substances,
Figure GDA0002744670550000051
is the sum of the squared errors, and,
Figure GDA0002744670550000052
in the form of a regression sum of squares,
Figure GDA0002744670550000053
is the average value of the values,
Figure GDA0002744670550000054
in the form of a mean square regression,
Figure GDA0002744670550000055
is the mean square residual error;
3 represents the degree of freedom of regression,
n-4 represents the degree of freedom of the error,
n-1 represents a total degree of freedom;
given a significance level α, when P0<At alpha, reject H0Consider Y and X1，X2，X3The linear regression relationship between the two is obvious; otherwise, the effect of each regression variable on Y by linear form is considered insignificant.
Further, the temperature rise prediction analysis module operates in the following manner, and every time the background monitoring management system obtains a new set of sensor perception data (x)n+1,1,xn+1,2,xn+1,3) Then let xn+1＝(1,xn+1,1,xn+1,2,xn+1,3)TSimultaneously, the temperature rise variable is predicted according to the linear regression equation
Figure GDA0002744670550000056
Wherein the content of the first and second substances,
Figure GDA0002744670550000057
is yn+1Is estimated at the point of time of (a),
the interval when the surrounding environment of the bus duct changes frequently is estimated as
Due to the fact that
Figure GDA0002744670550000061
Therefore, it is not only easy to use
Figure GDA0002744670550000062
Thus yn+1With a confidence interval of 1-alpha
Figure GDA0002744670550000063
The longer the confidence interval length, the less accurate the prediction.
Further, the residual differenceThe analysis module carries out normality test on the error items and sets errorsiThe estimated value of (i ═ 1, …, n) is
Figure GDA0002744670550000064
And is
Figure GDA0002744670550000065
The normal distribution is satisfied and,
Figure GDA0002744670550000066
wherein
Figure GDA0002744670550000067
The ith element on the main diagonal of H, so that the residual is
Figure GDA0002744670550000068
And the background monitoring and management system outputs the normal QQ diagram of the background monitoring and management system through the residual value, and if the scattered points of the normal QQ diagram are obviously distributed on a straight line, the error estimation meets the normal distribution, namely the prediction result of the temperature rise can be accepted.
The invention has the advantages that;
(1) the method has the advantages of reliable operation, simplicity, easy implementation and high prediction precision. Experimental data prove that the prediction error of the monitoring model can be controlled within the range of 1.5% -2%, the monitoring model has better feasibility and reliability, and can be applied and popularized in an actual power supply system, so that a novel intelligent bus system is established, and the intelligent level of the power supply system is improved;
(2) the temperature rise is monitored by the temperature rise linear regression module, the parameter significance testing module, the temperature rise prediction analysis module and the residual error analysis module, errors are compared, and the device is high in precision and good in operability.
Drawings
FIG. 1 is a schematic structural diagram of a bus duct temperature rise monitoring system based on a wireless sensor network according to the present invention;
Detailed Description
The present invention will now be described in further detail with reference to the accompanying drawings. These drawings are simplified schematic views illustrating only the basic structure of the present invention in a schematic manner, and thus show only the constitution related to the present invention.
As shown in fig. 1, a bus duct temperature rise monitoring system based on a wireless sensor network includes a plurality of bus duct branch lines arranged in parallel, a bus duct trunk line arranged at the upstream of the plurality of bus duct branch lines, a master switch, a data fusion center and a background monitoring management system, wherein the master switch is arranged between the bus duct trunk line and the data fusion center, each bus duct branch line is provided with a leakage switch, a lower computer and a sensor node, and each leakage switch is arranged between the downstream of the bus duct trunk line and each lower computer; the data fusion center is connected with the background monitoring management system and used for sending the acquired temperature information to a computer in the background monitoring management system for data analysis; the method is characterized in that: the background monitoring and management system comprises a temperature rise linear regression module, a parameter significance inspection module, a temperature rise prediction analysis module and a residual error analysis module.
Specifically, the following model is adopted in the temperature rise linear regression module to calculate the temperature rise of the bus duct
(yi,xi1,xi2,xi3),i＝1,…,n (1)
Wherein the content of the first and second substances,
yithe temperature of the bus duct rises,
xi1is the value of the current load and,
xi2is an average of a plurality of temperature values,
xi3is a humidity value;
and satisfy
yi＝β0+βxi1+β2xi2+β3xi3+i,i＝1,…,n (2)
Whereini(i-1, …, n) are regression constants, which are each otherIndependently and all follow normal distributioni～N(0,σ2)，
βiIs a regression coefficient;
using a matrix expression, order
Figure GDA0002744670550000081
The temperature rise linear regression is then formulated as
Y＝Xβ+ (4)
Wherein the matrix X is a temperature rise influence factor matrix, and X columns are full rank,
the matrix Y is a bus duct temperature rise matrix,
beta is a matrix of the regression coefficients,
is a regression constant matrix;
wherein the regression coefficient beta is estimated using least squares, i.e. beta is chosen such that the sum of the squares of the error terms
S(β)＝T＝(Y-Xβ)T(Y-Xβ) (5)
The minimum value is reached, and the minimum value,
because of the Hessian matrix of S (beta) at beta
Figure GDA0002744670550000091
Is a positive definite matrix, so its least squares estimation
Figure GDA0002744670550000092
I.e., the minimum point of S (β), where k and l are the abscissa and ordinate, respectively, at β, i.e., the point β (β)k，βl)。
Specifically, the sensor node detects the current load value of each bus duct branch line, and collects 6 temperature values of two phases of two sides of 3 joints of the bus duct except for N phases and humidity values around the bus duct at the same time.
Specifically, the regression coefficient β is recalculated every interval of time, and the regression coefficient β is not changed in adjacent 2 intervals.
Specifically, in the parameter significance checking module, a binary hypothesis is set for formula (4)
Figure GDA0002744670550000107
H1I is more than or equal to 1 and less than or equal to 3, so that beta isi≠0 (8)
Constructing F test statistics
Figure GDA0002744670550000101
Wherein the content of the first and second substances,
Figure GDA0002744670550000102
is the sum of the squared errors, and,
Figure GDA0002744670550000103
in the form of a regression sum of squares,
Figure GDA0002744670550000104
is the average value of the values,
Figure GDA0002744670550000105
in the form of a mean square regression,
Figure GDA0002744670550000106
is the mean square residual error;
3 represents the degree of freedom of regression,
n-4 represents the degree of freedom of the error,
n-1 represents a total degree of freedom;
given a significance level α, when P0If < alpha, reject H0Consider Y and X1，X2，X3The linear regression relationship between the two is obvious; otherwise, the effect of each regression variable on Y by linear form is considered insignificant.
Specifically, the temperature rise prediction analysis module operates in the following manner, whenever the background monitoring management system obtains a new set of sensor perception data (x)n+1，1，xn+1，2，xn+1，3) Then let xn+1＝(1，xn+1，1，xn+1，2，xn+1，3)TSimultaneously, the temperature rise variable is predicted according to the linear regression equation
Figure GDA0002744670550000111
Wherein the content of the first and second substances,
Figure GDA0002744670550000112
is yn+1Is estimated at the point of time of (a),
the interval when the surrounding environment of the bus duct changes frequently is estimated as
Due to the fact that
Figure GDA0002744670550000113
Therefore, it is not only easy to use
Figure GDA0002744670550000114
Thus yn+1With a confidence interval of 1-alpha
Figure GDA0002744670550000115
The longer the confidence interval length, the less accurate the prediction.
Specifically, the residual error analysis module carries out normality test on the error items and sets errorsiThe estimated value of (i ═ 1, …, n) is
Figure GDA0002744670550000116
And is
Figure GDA0002744670550000117
The normal distribution is satisfied and,
Figure GDA0002744670550000118
wherein
Figure GDA0002744670550000123
The ith element on the main diagonal of H, so that the residual is
Figure GDA0002744670550000121
And the background monitoring and management system outputs the normal QQ diagram of the background monitoring and management system through the residual value, and if the scattered points of the normal QQ diagram are obviously distributed on a straight line, the error estimation meets the normal distribution, namely the prediction result of the temperature rise can be accepted.
In order to verify the application effect of the bus duct temperature rise monitoring system, the indoor bus duct is installed in real time and monitored in real time at the room temperature of 25 ℃, and when the current is 2500A, the comparison between the actual temperature rise and the predicted temperature rise value is shown in Table 1.
TABLE 1 comparison table of actual temperature rise and predicted temperature rise value of bus duct
Figure GDA0002744670550000122
Figure GDA0002744670550000131
When the current load was 800A, the actual temperature rise was compared to the predicted value of temperature rise as shown in table 2.
TABLE 2 comparison table of actual temperature rise and predicted temperature rise value of bus duct
Sensor acquisition time Actual temperature rise Predicted value of temperature rise 95% confidence interval Error of the measurement
8:00 46.1 46.3 (46.28,46.32) 0.4％
9:00 46.4 46.2 (46.19,46.21) 0.4％
10:00 46.5 46.4 (46.38,46.42) 0.2％
11:00 46.4 46.7 (46.66,46.74) 0.6％
12:00 46.9 46.6 (46.57,46.63) 0.6％
1:00 47.3 47.1 (47.05,47.15) 0.4％
2:00 47.3 47.5 (47.45,47.55) 0.4％
3:00 47.1 46.8 (46.78,46.82) 0.6％
4:00 46.8 46.9 (46.86,46.94) 0.2％
5:00 46.2 46.5 (46.45,46.55) 0.6％
6:00 45.9 45.5 (45.48,45.52) 0.8％
The average relative error of temperature rise prediction is 0.2% -0.8% under the two current load conditions, and therefore in the technical scheme recorded in the application, the difference between the predicted temperature rise value and the actual temperature rise value is small, the prediction accuracy is high, and the requirement of bus duct temperature rise monitoring can be met.
In light of the foregoing description of the preferred embodiment of the present invention, many modifications and variations will be apparent to those skilled in the art without departing from the spirit and scope of the invention. The technical scope of the present invention is not limited to the content of the specification, and must be determined according to the scope of the claims.

Claims (6)

1. A bus duct temperature rise monitoring system based on a wireless sensor network comprises a plurality of bus duct branch lines arranged in parallel, a bus duct trunk line arranged at the upstream of the bus duct branch lines, a main switch, a data fusion center and a background monitoring management system, wherein the main switch is arranged between the bus duct trunk line and the data fusion center, each bus duct branch line is provided with a leakage switch, a lower computer and a sensor node, and each leakage switch is arranged between the downstream of the bus duct trunk line and each lower computer; the data fusion center is connected with the background monitoring management system and used for sending the acquired temperature information to a computer in the background monitoring management system for data analysis; the method is characterized in that: the background monitoring and management system comprises a temperature rise linear regression module, a parameter significance inspection module, a temperature rise prediction analysis module and a residual error analysis module;
the temperature rise linear regression module adopts the following model to calculate the temperature rise of the bus duct
(yi,xi1,xi2,xi3),i＝1,…,n (1)
Wherein the content of the first and second substances,
yithe temperature of the bus duct rises,
xi1is the value of the current load and,
xi2is an average of a plurality of temperature values,
xi3is a humidity value;
and satisfy
yi＝β0+βxi1+β2xi2+β3xi3+i,i＝1,…,n (2)
Whereini(i-1, …, n) are regression constants which are independent of each other and each obey a normal distributioni～N(0，σ2)，
βiIs a regression coefficient;
using a matrix expression, order
Figure FDA0002744670540000021
The temperature rise linear regression is then formulated as
Y＝Xβ+ (4)
Wherein the matrix X is a temperature rise influence factor matrix, and X columns are full rank,
the matrix Y is a bus duct temperature rise matrix,
beta is a matrix of the regression coefficients,
is a regression constant matrix;
wherein the regression coefficient beta is estimated using least squares, i.e. beta is chosen such that the sum of the squares of the error terms
S(β)＝T＝(Y-Xβ)T(Y-Xβ) (5)
The minimum value is reached, and the minimum value,
because of the Hessian matrix of S (beta) at beta
Figure FDA0002744670540000022
Is a positive definite matrix, so its least squares estimation
Figure FDA0002744670540000023
I.e., the minimum point of S (β), where k and l are the abscissa and ordinate, respectively, at β, i.e., the point β (β)k，βl)。
2. The system of claim 1, wherein: the sensor node detects the current load value of each bus duct branch line, and collects total 6 temperature values of two phases of two sides of 3 joints of the bus duct except N phases and humidity values around the bus duct simultaneously.
3. The system of claim 1, wherein: the regression coefficient β is recalculated with a period of time every interval, and the regression coefficient β is unchanged in the adjacent 2 intervals.
4. The system of claim 1, wherein: in the parameter significance checking module, a binary hypothesis is set for formula (4)
Figure FDA0002744670540000031
Constructing F test statistics
Figure FDA0002744670540000032
Wherein the content of the first and second substances,
Figure FDA0002744670540000033
is the sum of the squared errors, and,
Figure FDA0002744670540000034
in the form of a regression sum of squares,
Figure FDA0002744670540000035
is the average value of the values,
Figure FDA0002744670540000036
in the form of a mean square regression,
Figure FDA0002744670540000037
is the mean square residual error;
3 represents the degree of freedom of regression,
n-4 represents the degree of freedom of the error,
n-1 represents a total degree of freedom;
given a significance level α, when P0<At alpha, reject H0Consider Y and X1，X2，X3The linear regression relationship between the two is obvious; otherwise, the effect of each regression variable on Y by linear form is considered insignificant.
5. The system of claim 4, wherein: the temperature rise prediction analysis module operates in the following manner, and every time the background monitoring and management system obtains a new set of sensor perception data (x)n+1,1,xn+1,2,xn+1,3) Then let xn+1＝(1,xn+1,1,xn+1,2,xn+1,3)TSimultaneously, the temperature rise variable is predicted according to the linear regression equation
Figure FDA0002744670540000041
Wherein the content of the first and second substances,
Figure FDA0002744670540000042
is yn+1Is estimated at the point of time of (a),
when the surrounding environment of the bus duct changes frequently, the interval estimation is estimated as follows
Due to the fact that
Figure FDA0002744670540000043
Therefore, it is not only easy to use
Figure FDA0002744670540000044
Thus yn+1With a confidence interval of 1-alpha
Figure FDA0002744670540000045
The longer the confidence interval length, the less accurate the prediction.
6. The system of claim 5, wherein: the residual error analysis module carries out normality test on the error items and sets errorsiThe estimated value of (i ═ 1, …, n) is
Figure FDA0002744670540000046
And is
Figure FDA0002744670540000047
The normal distribution is satisfied and,
Figure FDA0002744670540000048
wherein
Figure FDA0002744670540000051
The ith element on the main diagonal of H, so that the residual is
Figure FDA0002744670540000052
And the background monitoring and management system outputs the normal QQ diagram of the background monitoring and management system through the residual value, and if the scattered points of the normal QQ diagram are obviously distributed on a straight line, the error estimation meets the normal distribution, namely the prediction result of the temperature rise can be accepted.
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