CN110188916B - System and method for estimating insulation performance of high-voltage electrical equipment - Google Patents
System and method for estimating insulation performance of high-voltage electrical equipment Download PDFInfo
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- CN110188916B CN110188916B CN201910301940.7A CN201910301940A CN110188916B CN 110188916 B CN110188916 B CN 110188916B CN 201910301940 A CN201910301940 A CN 201910301940A CN 110188916 B CN110188916 B CN 110188916B
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Abstract
The invention discloses a system and a method for estimating the insulation performance of high-voltage electrical equipment, wherein the system comprises a data acquisition module, a data storage module, a data analysis module and an estimation evaluation module, wherein the data acquisition module is connected with the data storage module, the data analysis module is connected with the data storage module, and the estimation evaluation module is connected with the data storage module and the data analysis module. The data acquisition module comprises a temperature acquisition module, a humidity acquisition module, a sea wind intensity acquisition module, a salt content acquisition module, a sea wave height acquisition module, a vibration frequency acquisition module and a dust concentration acquisition module, wherein the temperature acquisition module is used for acquiring the temperature of air on the sea surface, the humidity acquisition module is used for acquiring the humidity of the air on the sea surface, and the sea wind intensity acquisition module is used for acquiring the intensity of sea wind.
Description
Technical Field
The invention relates to the field of high-voltage electrical equipment, in particular to a system and a method for estimating the insulation performance of high-voltage electrical equipment.
Background
Electrical equipment is a generic term for equipment such as generators, transformers, power lines, and circuit breakers in a power system, and is high-voltage electrical equipment having a voltage level of 1000v or more. Many high-voltage electrical equipment is arranged on the ship, and in order to ensure that the high-voltage electrical equipment can safely run and the ship can normally run on the sea, the insulating performance of the high-voltage electrical equipment must be in a good state. In order to prolong the insulation performance of the high-voltage electrical equipment, the high-voltage electrical equipment on the ship is usually in the middle of a certain humidity, but the offshore environment is very bad, the insulation performance of the high-voltage electrical equipment cannot be fully ensured not to be influenced by the offshore environment by controlling the temperature and the humidity, the insulation performance of the high-voltage electrical equipment can not withstand the offshore bad environment at any time, and the insulation performance of the high-voltage electrical equipment cannot be estimated in the prior art.
Disclosure of Invention
The invention aims to provide a system and a method for estimating the insulation performance of high-voltage electrical equipment, which are used for solving the problems in the prior art.
In order to achieve the above purpose, the present invention provides the following technical solutions:
the system comprises a data acquisition module, a data storage module, a data analysis module and a prediction evaluation module, wherein the data acquisition module is connected with the data storage module, the data analysis module is connected with the data storage module, and the prediction evaluation module is connected with the data storage module and the data analysis module.
As a preferred scheme, the data acquisition module comprises a temperature acquisition module, a humidity acquisition module, a sea wind intensity acquisition module, a salt content acquisition module, a sea wave height acquisition module, a vibration frequency acquisition module and a dust concentration acquisition module, wherein the temperature acquisition module is used for acquiring the temperature of air on the sea surface, the humidity acquisition module is used for acquiring the humidity of the air on the sea surface, the sea wind intensity acquisition module is used for acquiring the intensity of sea wind, the salt content acquisition module comprises an offshore air salt content acquisition module and an electric equipment ambient air salt content acquisition module, the offshore air salt content acquisition module is used for acquiring the salt content of the air on the sea surface, the electric equipment ambient air salt content acquisition module is used for acquiring the salt content of the air around the electric equipment, the sea wave height acquisition module is used for acquiring the height of sea waves, the dust concentration acquisition module is used for acquiring the dust concentration around the electric equipment, and the vibration frequency acquisition module is used for acquiring the vibration frequency of a ship.
The data storage module comprises a temperature storage module, a humidity storage module, a sea air intensity storage module, a salt content storage module, a sea wave height storage module, a vibration frequency storage module and a dust concentration storage module, wherein the salt content storage module comprises an offshore atmosphere salt content storage module and an electrical equipment ambient air salt content storage module, the temperature storage module is connected with the temperature acquisition module, the humidity acquisition module is connected with the humidity storage module, the sea air intensity storage module and the sea air intensity acquisition module are connected, the sea atmosphere salt content storage module is connected with the offshore atmosphere salt content acquisition module, the electrical equipment ambient air salt content storage module is connected with the electrical equipment ambient air salt content acquisition module, the sea wave height storage module is connected with the sea wave height acquisition module, the vibration frequency storage module is connected with the vibration frequency acquisition module, the dust concentration storage module is connected with the dust concentration acquisition module, the temperature storage module, the humidity storage module, the sea air intensity storage module, the salt content storage module, the sea wave height storage module, the vibration frequency storage module and the dust concentration storage module are all connected with the data analysis module, the data analysis module is used for analyzing insulation performance related data of historical days and estimated insulation performance related data and estimated weather insulation performance estimated performance related data, and the estimated insulation performance estimated performance is used for estimating the high-performance estimated piezoelectric equipment.
A method for predicting insulation performance of high-voltage electrical equipment, the method comprising the steps of:
(1) Collecting relevant data of the insulation performance of the electrical equipment in the historical days;
(2) Acquiring related data of the insulation performance of the electrical equipment in the estimated day;
(3) Analyzing the similarity between the insulating performance related data of the historical on-day electrical equipment and the estimated insulating performance related data of the on-day electrical equipment;
(4) And calculating the insulation performance of the estimated weather electric equipment.
Preferably, the estimating method includes the following steps:
(1) The data acquisition module sets a certain time period in one day as an acquisition time period, divides the acquisition time period into 3 small time periods T1, T2 and T3, acquires insulation performance related data of the electrical equipment in the three small time periods in the historical days, and stores the data into the data storage module;
(2) Obtaining each of three estimated days T1, T2, T3 from the ocean forecast
Average temperature A0[ A0 (1), A0 (2), A0 (3) ]
Average humidity B0[ B0 (1), B0 (2), B0 (3) ]
Average value of sea wind intensity C0[ C0 (1), C0 (2), C0 (3) ]
Average value D0[ D0 (1), D0 (2), D0 (3) ] of salt content of marine atmosphere
Average value of sea height E0[ E0 (1), E0 (2), E0 (3) ];
(3) The data analysis module reads the data in the data storage module and the data obtained from the ocean forecast, analyzes and calculates the data, and selects historical days similar to the estimated days;
(4) The estimated evaluation module calculates and outputs an estimated insulation performance estimated value of the electrical equipment, and judges the state of the insulation performance of the electrical equipment according to the insulation performance estimated value.
Preferably, the step (1) includes the following steps:
1) The temperature acquisition module acquires the average temperature of each of three small time periods T1, T2 and T3 in the past N days adjacent to the estimated day, and forms a group of temperature vectors:
an [ An (1), an (2), an (3) ], wherein n= … N, and storing the segment temperature vector to the temperature storage module;
2) The humidity acquisition module acquires the average humidity of each of three small time periods T1, T2 and T3 in the past N days adjacent to the estimated day, and forms a group of section humidity vectors:
bn [ Bn (1), bn (2), bn (3) ], where n= … N, and storing the segment humidity vector to the humidity storage module;
3) The sea wind intensity acquisition module acquires the average value of the sea wind intensity of each of three time periods T1, T2 and T3 in the past N days adjacent to the estimated day, and forms a group of sea wind intensity vectors:
cn [ Cn (1), cn (2), cn (3) ], wherein n= … N, and storing the segment sea wind intensity vector to the sea wind intensity storage module;
4) The marine atmosphere salt content acquisition module acquires the average value of the marine atmosphere salt content of each of three small time periods T1, T2 and T3 in the past N days adjacent to the estimated day, and forms a group of first salt content vectors:
dn [ Dn (1), dn (2), dn (3) ], wherein n= … N, and storing the segment first salinity vector to an offshore atmospheric salinity storage module;
5) The sea wave height acquisition module acquires the average value of the sea wave heights of each of three small time periods T1, T2 and T3 in the past N days adjacent to the estimated day, and forms a group of sea wave height vectors:
en [ En (1), en (2), en (3) ], wherein n= … N, and storing the segment sea wave height vector to the sea wave height storage module;
6) The salt content acquisition module of the surrounding air of the electrical equipment acquires the average value of the salt content in the surrounding air of the electrical equipment in each of three small time periods of T1, T2 and T3 in the past N days adjacent to the estimated day, and forms a group of second salt content vectors:
fn [ Fn (1), fn (2), fn (3) ], wherein n= … N, and storing the segment second salt content vector to the electrical equipment ambient air salt content storage module
7) The dust concentration acquisition module acquires the average value of dust concentration around the electrical equipment in each of three small time periods T1, T2 and T3 in the past N days adjacent to the estimated day, and forms a group of dust concentration vectors:
gn [ Gn (1), gn (2), gn (3) ], wherein n= … N, and storing the segment dust concentration vector to the dust concentration storage module;
8) The vibration frequency acquisition module acquires the average value of the vibration frequency of the electrical equipment in each of three small time periods T1, T2 and T3 in the past N days adjacent to the estimated day, and forms a group of section vibration frequency vectors:
hn [ Hn (1), hn (2), hn (3) ], where n= … N, and stores the segment vibration frequency vector to the vibration frequency storage module.
In the technical scheme, the temperature and the humidity around the high-voltage electrical equipment are not required to be collected, and the temperature and the humidity monitoring equipment is arranged around the high-voltage electrical equipment in the prior art, so that the temperature and the humidity of the electrical equipment are within a certain range, the influence of the temperature and the humidity on the insulation performance of the electrical equipment is small, and the influence of the temperature and the humidity on the insulation performance of the electrical equipment is not considered; when the ship runs on the sea, the ship can vibrate to a certain extent due to sea wind and sea waves, so that the electric equipment vibrates, mechanical force is generated on the electric equipment, and the mechanical force can damage the insulation performance of the high-voltage electric equipment; the sea wind contains salt, and the salt can erode the shell of the high-voltage electric equipment, so that the insulating performance of the high-voltage electric equipment is affected; there is some dust in the air, which may cover the surface of the electrical equipment and also reduce the insulation performance of the high-voltage electrical equipment.
Preferably, the step (3) includes the following steps:
1) The data analysis module reads the segment temperature vector In the temperature acquisition module and the average temperature of the estimated day In the ocean forecast, calculates the difference value between the average temperature of the estimated day and the average temperature of the past N days adjacent to the estimated day In three small time periods of T1, T2 and T3, weights the two to obtain a temperature measurement value In, and then:
In＝i1abs(A0(1)-An(1))+i2abs(A0(2)-An(2))+i3abs(A0(3)-An(3))，
n= … N, i1+i2+i3=1, i1, i2, i3 are all greater than 0, abs (…) represents the difference between the average temperature on the estimated day and the absolute value of the average temperature on the past N days adjacent to the estimated day;
2) The data analysis module reads the segment humidity vector In the humidity acquisition module and the average humidity of the estimated days In the ocean forecast, calculates the difference value between the average humidity of the estimated days and the absolute value of the average humidity of the past N days adjacent to the estimated days In every three small time periods of T1, T2 and T3, weights the absolute value and obtains a humidity measurement value In, and then:
Jn＝j1abs(B0(1)-Bn(1))+j2abs(B0(2)-Bn(2))+j3abs(B0(3)-Bn(3))，
n= … N, j1+j2+j3=1, j1, j2, j3 are all greater than 0, abs (…) represents the difference between the average humidity on the estimated day and the absolute value of the average humidity on the past N days adjacent to the estimated day;
3) The data analysis module reads the segment sea wind intensity vector in the sea wind intensity acquisition module and the average value of the sea wind intensity of the estimated day in the sea forecast, calculates the difference value between the average value of the sea wind intensity of the estimated day and the absolute value of the average value of the sea wind intensity of the past N days close to the estimated day in each three small time periods of T1, T2 and T3, weights the difference value to obtain a sea wind intensity measurement value Kn, and then:
Kn＝k1abs(C0(1)-Cn(1))+k2abs(C0(2)-Cn(2))+k3abs(C0(3)-Cn(3))，
n= … N, k1+k2+k3=1, k1, k2, k3 are all greater than 0, abs (…) represents the difference between the average of the sea wind intensities on the estimated day and the average of the sea wind intensities on the past N days adjacent to the estimated day;
4) The data analysis module reads the first salt content vector of the section in the marine atmosphere salt content storage module and the average value of the marine atmosphere salt content of the estimated day in the marine forecast, calculates the difference value between the average value of the marine atmosphere salt content of the estimated day and the average value of the marine atmosphere salt content of the past N days close to the estimated day in each three small time periods of T1, T2 and T3, weights the average value of the marine atmosphere salt content of the estimated day and the average value of the marine atmosphere salt content of the past N days close to the estimated day, and obtains a salt content measurement value Ln, and then: ln=l1abs (D0 (1) -Dn (1)) +l2abs (D0 (2) -Dn (2)) +l3abs (D0 (3) -Dn (3)),
n= … N, l1+l2+l3=1, l1, l2, l3 are all greater than 0, abs (…) represents the difference between the average value of the estimated marine atmospheric salt content on the day and the average value of the marine atmospheric salt content on the past N days adjacent to the estimated day;
5) The data analysis module reads the segment wave height vector in the wave height storage module and the average value of the wave heights of the estimated days in the ocean forecast, calculates the difference value between the average value of the wave heights of the estimated days and the average value of the wave heights of the past N days close to the estimated days in each three small time periods of T1, T2 and T3, weights the difference value to obtain a wave height measurement value Mn, and then:
Mn＝m1abs(E0(1)-En(1))+m2abs(E0(2)-En(2))+m3abs(E0(3)-En(3))，
n= … N, m1+m2+m3=1, m1, m2, m3 are all greater than 0, abs (…) represents the difference between the average of the sea wave heights on the estimated day and the average of the sea wave heights on the past N days adjacent to the estimated day;
6) The data analysis module brings the values of In, jn, kn, ln and Mn into the calculation of the salt content similarity evaluation value Xn and the vibration frequency similarity evaluation value Yn respectively, and sorts the salt content similarity evaluation value Xn and the vibration frequency similarity evaluation value Yn from small to large respectively.
Preferably, step 6) in step (3) includes the steps of:
a) The data analysis module calculates a salt content similarity evaluation value Xn and a vibration frequency similarity evaluation value Yn of the past N days adjacent to the estimated day, and then:
Xn＝x1In+x2Jn+x3Kn+x4Ln，
yn=y1kn+y2mn, n= … N, x1, x2, x3, x4, y1, y2 are all constants;
b) The data analysis module respectively sorts the salt content similarity evaluation value Xn and the vibration frequency similarity evaluation value Yn from small to large, and respectively takes Xn and Yn of the first three arranged sequences as the similar days of the salt content and the similar days of the vibration frequency of the estimated days.
Preferably, the step (4) includes the following steps:
1) The estimated evaluation module calculates a salt content estimated value U, a dust concentration estimated value V and a vibration frequency estimated value W
U＝0.7U1+0.2U2+0.1U3，
U1 is the average value of the salt concentration on the day where the salt content similarity evaluation value is smallest, U2 is the average value of the salt concentration on the day where the salt content similarity evaluation value is second smallest, and U3 is the average value of the salt concentration on the day where the salt content similarity evaluation value is third smallest;
V＝(G1(1)+G1(2)+G1(3)+…+GN(1)+GN(1)+GN(3))/3N，
W＝0.7W1+0.2W2+0.1W3，
w1 is an average value of the vibration frequency on the day where the vibration frequency similarity evaluation value is smallest, W2 is an average value of the vibration frequency on the day where the vibration frequency similarity evaluation value is second smallest, and W3 is an average value of the vibration frequency on the day where the vibration frequency similarity evaluation value is third smallest;
2) Calculating and evaluating estimated insulation performance predictive value Z=z1U+z2V+z3W of the estimated electrical equipment, wherein Z1, Z2 and Z3 are all constants,
if Z2 is smaller than Z0, the insulation performance of the electrical equipment is good in estimated days, and the electrical equipment can be normally used;
if Z2 is greater than or equal to Z0, this indicates that there is a risk of insulating performance of the electrical equipment on the estimated day, and maintenance should be performed on the electrical equipment, and the electrical equipment should be carefully used.
As a preferable scheme, the value of N is 5,N which is not too large or too small, the difficulty of calculation is increased, and the accuracy of the insulating property estimated value is reduced.
Compared with the prior art, the invention has the beneficial effects that: according to the invention, the insulation performance condition of the high-voltage electrical equipment on the estimated day is effectively estimated and evaluated through the estimating system and the estimating method, when hidden danger exists in the insulation performance of the estimated high-voltage electrical equipment, operators on the ship are reminded to maintain the insulation performance of the high-voltage electrical equipment, and the operators are reminded to carefully use the high-voltage electrical equipment, so that the high-voltage electrical equipment can safely run, the personal safety of the operators on the ship is also ensured, and the ship can normally run on the sea.
Drawings
FIG. 1 is a schematic block diagram of a system for estimating insulation performance of a high voltage electrical apparatus according to the present invention;
FIG. 2 is a schematic diagram of a connection structure of a system for estimating insulation performance of high-voltage electrical equipment according to the present invention;
FIG. 3 is a schematic flow chart of a method for estimating insulation performance of high-voltage electrical equipment according to the present invention;
FIG. 4 is a schematic flow chart of step (1) of a method for estimating insulation performance of high-voltage electrical equipment according to the present invention;
FIG. 5 is a schematic flow chart of step (3) of a method for estimating insulation performance of high-voltage electrical equipment according to the present invention;
fig. 6 is a schematic flow chart of step (4) of a method for estimating insulation performance of high-voltage electrical equipment according to the present invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Referring to fig. 1 to 6, in an embodiment of the present invention, an insulation performance prediction system for a high-voltage electrical apparatus includes a data acquisition module, a data storage module, a data analysis module, and a prediction evaluation module, where the data acquisition module is connected to the data storage module, the data analysis module is connected to the data storage module, and the prediction evaluation module is connected to the data storage module and the data analysis module.
As a preferred scheme, the data acquisition module comprises a temperature acquisition module, a humidity acquisition module, a sea wind intensity acquisition module, a salt content acquisition module, a sea wave height acquisition module, a vibration frequency acquisition module and a dust concentration acquisition module, wherein the temperature acquisition module is used for acquiring the temperature of air on the sea surface, the humidity acquisition module is used for acquiring the humidity of the air on the sea surface, the sea wind intensity acquisition module is used for acquiring the intensity of sea wind, the salt content acquisition module comprises an offshore air salt content acquisition module and an electric equipment ambient air salt content acquisition module, the offshore air salt content acquisition module is used for acquiring the salt content of the air on the sea surface, the electric equipment ambient air salt content acquisition module is used for acquiring the salt content of the air around the electric equipment, the sea wave height acquisition module is used for acquiring the height of sea waves, the dust concentration acquisition module is used for acquiring the dust concentration around the electric equipment, and the vibration frequency acquisition module is used for acquiring the vibration frequency of a ship.
The data storage module comprises a temperature storage module, a humidity storage module, a sea air intensity storage module, a salt content storage module, a sea wave height storage module, a vibration frequency storage module and a dust concentration storage module, wherein the salt content storage module comprises an offshore atmosphere salt content storage module and an electrical equipment ambient air salt content storage module, the temperature storage module is connected with the temperature acquisition module, the humidity acquisition module is connected with the humidity storage module, the sea air intensity storage module and the sea air intensity acquisition module are connected, the sea atmosphere salt content storage module is connected with the offshore atmosphere salt content acquisition module, the electrical equipment ambient air salt content storage module is connected with the electrical equipment ambient air salt content acquisition module, the sea wave height storage module is connected with the sea wave height acquisition module, the vibration frequency storage module is connected with the vibration frequency acquisition module, the dust concentration storage module is connected with the dust concentration acquisition module, the temperature storage module, the humidity storage module, the sea air intensity storage module, the salt content storage module, the sea wave height storage module, the vibration frequency storage module and the dust concentration storage module are all connected with the data analysis module, the data analysis module is used for analyzing insulation performance related data of historical days and estimated insulation performance related data and estimated weather insulation performance estimated performance related data, and the estimated insulation performance estimated performance is used for estimating the high-performance estimated piezoelectric equipment.
A method for estimating the insulation performance of high-voltage electrical equipment comprises the following steps:
(1) The data acquisition module sets 6-18 points in one day as an acquisition time period, divides the acquisition time period (6-18 points) into 3 small time periods T1 (6-10 points), T2 (10-14 points) and T3 (14-18 points), acquires data related to the insulation performance of the electrical equipment in the three small time periods in the historical days, and stores the data into the data storage module:
1) The temperature acquisition module acquires the average temperature of each of three small time periods T1, T2 and T3 in the past 5 days adjacent to the estimated day, and forms a group of temperature vectors:
a1[22,25,23], A2[22,26,23], A3[24,28,25], A4[24,27,25], A5[24,28,26] and storing the segment temperature vector to the temperature storage module;
2) The humidity acquisition module acquires the average humidity of each of three small time periods T1, T2 and T3 in the past 5 days adjacent to the estimated day, and forms a group of section humidity vectors:
b1[0.85,0.82,0.83], B2[0.84,0.83,0.83], B3[0.85,0.84,0.84], B4[0.83, 0.84] B1[0.85,0.84,0.85], and storing the segment humidity vector to a humidity storage module;
3) The sea wind intensity acquisition module acquires the average value of the sea wind intensity of each of three time periods T1, T2 and T3 in the last 5 days adjacent to the estimated day, and forms a group of sea wind intensity vectors:
c1[2, 3], C2[3,4,3], C3[3, 4], C4[4,5,4], C5[4, 4] and storing the segment sea wind intensity vector to a sea wind intensity storage module;
4) The marine atmosphere salt content acquisition module acquires the average value of the marine atmosphere salt content of each of three small time periods T1, T2 and T3 in the past 5 days adjacent to the estimated day, and forms a group of first salt content vectors:
d1[0.911,0.912,0.912], D2[0.911,0.912,0.911], D3[0.912,0.912,0.912], D4[0.912,0.912,0.911], D5[0.911,0.911,0.911], and storing the segment first salinity vector to an offshore atmospheric salinity storage module;
5) The sea wave height acquisition module acquires the average value of the sea wave heights of each of three small time periods T1, T2 and T3 in the past 5 days adjacent to the estimated day, and forms a group of sea wave height vectors:
e1[0.3,0.4,0.4], E2[0.4,0.5,0.4], E3[0.4,0.4,0.3], E4[0.5,0.6,0.5], E5[0.5,0.5,0.4] and storing the segment sea wave height vector to a sea wave height storage module;
6) The salt content acquisition module of the surrounding air of the electrical equipment acquires the average value of the salt content in the surrounding air of the electrical equipment in each of three small time periods T1, T2 and T3 in the past 5 days adjacent to the estimated day, and forms a group of second salt content vectors:
f1[0.899,0.9,0.899], F2[0.899,0.9,0.9], F3[0.9,0.9,0.899], F4[0.9,0.901,0.899], F5[0.9,0.9,0.9], and storing the segment second salt content vector to an air surrounding the electrical device salt content storage module
7) The dust concentration acquisition module acquires the average value of dust concentration around the electrical equipment in each of three small time periods T1, T2 and T3 in the last 5 days adjacent to the estimated day, and forms a group of dust concentration vectors:
g1[0.81,0.79,0.82], g1[0.8,0.79,0.78], G1[0.81,0.8,0.8], G1[0.78,0.8,0.8], G1[0.81,0.81,0.8], and storing the segment dust concentration vector to a dust concentration storage module;
8) The vibration frequency acquisition module acquires an average value of vibration frequencies of the electrical equipment in each of three small time periods T1, T2 and T3 in the past 5 days adjacent to the estimated day, and forms a group of section vibration frequency vectors:
h1[11,16,13], H2[15,17,16], H3[15,16,14], H4[16,19,17], H5[15,18,14], and storing the segment vibration frequency vector to the vibration frequency storage module
(2) Obtaining each of three estimated days T1, T2, T3 from the ocean forecast
Average temperature A0[23,25,24]
Average humidity B0[0.84,0.83,0.83]
Average value C0[4, 4] of sea wind intensity
Average value D0 of offshore atmosphere salt content [0.911,0.912,0.911]
Average value E0[0.4,0.5,0.4] of sea height;
(3) The data analysis module reads the data in the data storage module and the data obtained from the weather forecast, analyzes and calculates the data, and selects a historical day similar to the weather of the estimated day:
1) Calculating the difference between the average temperature of the estimated day and the average temperature of the last 5 days adjacent to the estimated day In every three small time periods T1, T2 and T3, and weighting the absolute values to obtain a temperature measurement value In, wherein:
I1＝0.6，I2＝1，I3＝1.8，I4＝1.4，I5＝2.1
2) Calculating the difference between the absolute value of the average humidity of the estimated day and the average humidity of the last 5 days adjacent to the estimated day in every three small time periods T1, T2 and T3, and weighting the absolute value to obtain a humidity measurement value Jn, wherein the humidity measurement value Jn is:
J1＝0.007，J2＝0，J3＝0.01，J4＝0.006，J5＝,13..013，
3) Calculating the difference between the average value of the sea wind intensity of the estimated day and the average value of the sea wind intensity of the past 5 days close to the estimated day in each three small time periods T1, T2 and T3, and weighting the absolute values to obtain a sea wind intensity measurement value Kn, wherein:
K1＝1.3，K2＝0.6，K3＝0.3，K4＝0.4，K5＝0，
4) Calculating the difference value between the average value of the marine atmospheric salt content of the estimated day and the average value of the marine atmospheric salt content of the past 5 days close to the estimated day in each three small time periods T1, T2 and T3, and weighting the absolute value to obtain a humidity measurement value Ln, wherein the humidity measurement value Ln is:
L1＝0.003，L2＝0，L3＝0.006，L4＝0.003，L5＝0.004
5) Calculating the difference between the average value of the sea wave heights of the estimated days and the average value of the sea wave heights of the last 5 days adjacent to the estimated days in each three small time periods T1, T2 and T3, and weighting the absolute values to obtain a humidity measurement value Mn, wherein the humidity measurement value Mn is:
M1＝0.06，M2＝0，M3＝0.07，M4＝0.1，M5＝0.06，
(4) The estimated evaluation module calculates and outputs an estimated insulation performance estimated value of the electrical equipment, and judges the state of the insulation performance of the electrical equipment according to the insulation performance estimated value:
1) Taking the values of In, jn, kn, ln and Mn into the calculation of the salt content similarity evaluation value Xn and the vibration frequency similarity evaluation value Yn, respectively, then:
X1＝I1+100J1+K1+100L1＝2.9，X2＝1.6，X3＝3.7,X4＝2.7,X5＝3.8
Y1＝Kn+5Mn＝1.6，Y2＝0.6，Y3＝0.65，Y4＝0.9,Y1＝0.3
2) Sorting Xn and Yn from small to large, and calculating a salt content predicted value U, a dust concentration predicted value V and a vibration frequency predicted value W according to the sorting
U＝0.7U1+0.2U2+0.1U3＝1.70902，
U1 is the average value of the salt concentration on the day where the salt content similarity evaluation value is smallest, U2 is the average value of the salt concentration on the day where the salt content similarity evaluation value is second smallest, and U3 is the average value of the salt concentration on the day where the salt content similarity evaluation value is third smallest;
V＝(G1(1)+G1(2)+G1(3)+…+G5(1)+G5(2)+G5(3))/15＝0.8，
W＝0.7W1+0.2W2+0.1W3＝15.669，
w1 is an average value of the vibration frequency on the day where the vibration frequency similarity evaluation value is smallest, W2 is an average value of the vibration frequency on the day where the vibration frequency similarity evaluation value is second smallest, and W3 is an average value of the vibration frequency on the day where the vibration frequency similarity evaluation value is third smallest;
3) Calculating estimated insulation performance predictive value z=0.5u+0.4v+0.1w=2.74141 of the electrical equipment, the value of Z0 is 3,
z2 is less than 3.2, which indicates that the electrical equipment has good insulation performance on estimated days, and the electrical equipment can be used normally.
It will be evident to those skilled in the art that the invention is not limited to the details of the foregoing illustrative embodiments, and that the present invention may be embodied in other specific forms without departing from the spirit or essential characteristics thereof. The present embodiments are, therefore, to be considered in all respects as illustrative and not restrictive, the scope of the invention being indicated by the appended claims rather than by the foregoing description, and all changes which come within the meaning and range of equivalency of the claims are therefore intended to be embraced therein. Any reference sign in a claim should not be construed as limiting the claim concerned.
Claims (5)
1. A method for estimating the insulation performance of high-voltage electrical equipment is characterized in that: the estimation method comprises the following steps:
(1) Collecting relevant data of the insulation performance of the electrical equipment in the historical days;
(2) Acquiring related data of the insulation performance of the electrical equipment in the estimated day;
(3) Analyzing the similarity between the insulating performance related data of the historical on-day electrical equipment and the estimated insulating performance related data of the on-day electrical equipment;
(4) Calculating and estimating the insulation performance of the weather-resistant equipment;
(5) The estimation method comprises the following steps:
(1) The data acquisition module sets a certain time period in one day as an acquisition time period, divides the acquisition time period into 3 small time periods T1, T2 and T3, acquires insulation performance related data of the electrical equipment in the three small time periods in the historical days, and stores the data into the data storage module;
(2) Obtaining each of three estimated days T1, T2, T3 from the ocean forecast
Average temperature A0[ A0 (1), A0 (2), A0 (3) ]
Average humidity B0[ B0 (1), B0 (2), B0 (3) ]
Average value of sea wind intensity C0[ C0 (1), C0 (2), C0 (3) ]
Average value D0[ D0 (1), D0 (2), D0 (3) ] of salt content of marine atmosphere
Average value of sea height E0[ E0 (1), E0 (2), E0 (3) ];
(3) The data analysis module reads the data in the data storage module and the data obtained from the ocean forecast, analyzes and calculates the data, and selects historical days similar to the estimated days;
(4) The estimated evaluation module calculates and outputs an estimated insulation performance estimated value of the electrical equipment, and judges the state of the insulation performance of the electrical equipment according to the insulation performance estimated value;
the step (1) comprises the following steps:
1) The temperature acquisition module acquires the average temperature of each of three small time periods T1, T2 and T3 in the past N days adjacent to the estimated day, and forms a group of temperature vectors:
an [ An (1), an (2), an (3) ], wherein n= … N, and storing the segment temperature vector to the temperature storage module;
2) The humidity acquisition module acquires the average humidity of each of three small time periods T1, T2 and T3 in the past N days adjacent to the estimated day, and forms a group of section humidity vectors:
bn [ Bn (1), bn (2), bn (3) ], where n= … N, and storing the segment humidity vector to the humidity storage module;
3) The sea wind intensity acquisition module acquires the average value of the sea wind intensity of each of three time periods T1, T2 and T3 in the past N days adjacent to the estimated day, and forms a group of sea wind intensity vectors:
cn [ Cn (1), cn (2), cn (3) ], wherein n= … N, and storing the segment sea wind intensity vector to the sea wind intensity storage module;
4) The marine atmosphere salt content acquisition module acquires the average value of the marine atmosphere salt content of each of three small time periods T1, T2 and T3 in the past N days adjacent to the estimated day, and forms a group of first salt content vectors:
dn [ Dn (1), dn (2), dn (3) ], wherein n= … N, and storing the segment first salinity vector to an offshore atmospheric salinity storage module;
5) The sea wave height acquisition module acquires the average value of the sea wave heights of each of three small time periods T1, T2 and T3 in the past N days adjacent to the estimated day, and forms a group of sea wave height vectors:
en [ En (1), en (2), en (3) ], wherein n= … N, and storing the segment sea wave height vector to the sea wave height storage module;
6) The salt content acquisition module of the surrounding air of the electrical equipment acquires the average value of the salt content in the surrounding air of the electrical equipment in each of three small time periods of T1, T2 and T3 in the past N days adjacent to the estimated day, and forms a group of second salt content vectors:
fn [ Fn (1), fn (2), fn (3) ], wherein n= … N, and storing the segment second salt content vector to the electrical equipment ambient air salt content storage module
7) The dust concentration acquisition module acquires the average value of dust concentration around the electrical equipment in each of three small time periods T1, T2 and T3 in the past N days adjacent to the estimated day, and forms a group of dust concentration vectors:
gn [ Gn (1), gn (2), gn (3) ], wherein n= … N, and storing the segment dust concentration vector to the dust concentration storage module;
8) The vibration frequency acquisition module acquires the average value of the vibration frequency of the electrical equipment in each of three small time periods T1, T2 and T3 in the past N days adjacent to the estimated day, and forms a group of section vibration frequency vectors:
hn [ Hn (1), hn (2), hn (3) ], where n= … N, and storing the segment vibration frequency vector to the vibration frequency storage module;
the step (3) comprises the following steps:
1) The data analysis module reads the segment temperature vector In the temperature acquisition module and the average temperature of the estimated day In the ocean forecast, calculates the difference value between the average temperature of the estimated day and the average temperature of the past N days adjacent to the estimated day In three small time periods of T1, T2 and T3, weights the two to obtain a temperature measurement value In, and then:
In＝i1abs(A0(1)-An(1))+i2abs(A0(2)-An(2))+i3abs(A0(3)-An(3))，
n= … N, i1+i2+i3=1, i1, i2, i3 are all greater than 0, abs (…) represents the difference between the average temperature on the estimated day and the absolute value of the average temperature on the past N days adjacent to the estimated day;
2) The data analysis module reads the segment humidity vector In the humidity acquisition module and the average humidity of the estimated days In the ocean forecast, calculates the difference value between the average humidity of the estimated days and the absolute value of the average humidity of the past N days adjacent to the estimated days In every three small time periods of T1, T2 and T3, weights the absolute value and obtains a humidity measurement value In, and then:
Jn＝j1abs(B0(1)-Bn(1))+j2abs(B0(2)-Bn(2))+j3abs(B0(3)-Bn(3))，
n= … N, j1+j2+j3=1, j1, j2, j3 are all greater than 0, abs (…) represents the difference between the average humidity on the estimated day and the absolute value of the average humidity on the past N days adjacent to the estimated day;
3) The data analysis module reads the segment sea wind intensity vector in the sea wind intensity acquisition module and the average value of the sea wind intensity of the estimated day in the sea forecast, calculates the difference value between the average value of the sea wind intensity of the estimated day and the absolute value of the average value of the sea wind intensity of the past N days close to the estimated day in each three small time periods of T1, T2 and T3, weights the difference value to obtain a sea wind intensity measurement value Kn, and then:
Kn＝k1abs(C0(1)-Cn(1))+k2abs(C0(2)-Cn(2))+k3abs(C0(3)-Cn(3))，
n= … N, k1+k2+k3=1, k1, k2, k3 are all greater than 0, abs (…) represents the difference between the average of the sea wind intensities on the estimated day and the average of the sea wind intensities on the past N days adjacent to the estimated day;
4) The data analysis module reads the first salt content vector of the section in the marine atmosphere salt content storage module and the average value of the marine atmosphere salt content of the estimated day in the marine forecast, calculates the difference value between the average value of the marine atmosphere salt content of the estimated day and the average value of the marine atmosphere salt content of the past N days close to the estimated day in each three small time periods of T1, T2 and T3, weights the average value of the marine atmosphere salt content of the estimated day and the average value of the marine atmosphere salt content of the past N days close to the estimated day, and obtains a salt content measurement value Ln, and then: ln=l1abs (D0 (1) -Dn (1)) +l2abs (D0 (2) -Dn (2)) +l3abs (D0 (3) -Dn (3)),
n= … N, l1+l2+l3=1, l1, l2, l3 are all greater than 0, abs (…) represents the difference between the average value of the estimated marine atmospheric salt content on the day and the average value of the marine atmospheric salt content on the past N days adjacent to the estimated day;
5) The data analysis module reads the segment wave height vector in the wave height storage module and the average value of the wave heights of the estimated days in the ocean forecast, calculates the difference value between the average value of the wave heights of the estimated days and the average value of the wave heights of the past N days close to the estimated days in each three small time periods of T1, T2 and T3, weights the difference value to obtain a wave height measurement value Mn, and then:
Mn＝m1abs(E0(1)-En(1))+m2abs(E0(2)-En(2))+m3abs(E0(3)-En(3))，
n= … N, m1+m2+m3=1, m1, m2, m3 are all greater than 0, abs (…) represents the difference between the average of the sea wave heights on the estimated day and the average of the sea wave heights on the past N days adjacent to the estimated day;
6) The data analysis module brings the values of In, jn, kn, ln and Mn into the calculation of the salt content similarity evaluation value Xn and the vibration frequency similarity evaluation value Yn respectively, and sorts the salt content similarity evaluation value Xn and the vibration frequency similarity evaluation value Yn from small to large respectively;
step 6) in step (3) comprises the steps of:
a) The data analysis module calculates a salt content similarity evaluation value Xn and a vibration frequency similarity evaluation value Yn of the past N days adjacent to the estimated day, and then:
Xn＝x1In+x2Jn+x3Kn+x4Ln，
yn=y1kn+y2mn, n= … N, x1, x2, x3, x4, y1, y2 are all constants;
b) The data analysis module respectively sorts the salt content similarity evaluation value Xn and the vibration frequency similarity evaluation value Yn from small to large, and respectively takes Xn and Yn of the first three arranged sequences as similar days of the salt content and similar days of the vibration frequency of the estimated days;
the step (4) comprises the following steps:
1) The estimated evaluation module calculates a salt content estimated value U, a dust concentration estimated value V and a vibration frequency estimated value W
U＝0.7U1+0.2U2+0.1U3，
U1 is the average value of the salt concentration on the day where the salt content similarity evaluation value is smallest, U2 is the average value of the salt concentration on the day where the salt content similarity evaluation value is second smallest, and U3 is the average value of the salt concentration on the day where the salt content similarity evaluation value is third smallest;
V＝(G1(1)+G1(2)+G1(3)+…+GN(1)+GN(1)+GN(3))/3N，
W＝0.7W1+0.2W2+0.1W3，
w1 is an average value of the vibration frequency on the day where the vibration frequency similarity evaluation value is smallest, W2 is an average value of the vibration frequency on the day where the vibration frequency similarity evaluation value is second smallest, and W3 is an average value of the vibration frequency on the day where the vibration frequency similarity evaluation value is third smallest;
2) Calculating and evaluating estimated insulation performance predictive value Z=z1U+z2V+z3W of the estimated electrical equipment, wherein Z1, Z2 and Z3 are all constants,
if Z2 is smaller than Z0, the insulation performance of the electrical equipment is good in estimated days, and the electrical equipment can be normally used;
if Z2 is greater than or equal to Z0, this indicates that there is a risk of insulating performance of the electrical equipment on the estimated day, and maintenance should be performed on the electrical equipment, and the electrical equipment should be carefully used.
2. The method for estimating insulation performance of high-voltage electrical equipment according to claim 1, wherein: the value of N is 5.
3. A high-voltage electrical equipment insulation performance estimation system for implementing the insulation performance estimation method for high-voltage electrical equipment according to claim 1, characterized in that: the estimating system comprises a data acquisition module, a data storage module, a data analysis module and an estimating and evaluating module, wherein the data acquisition module is connected with the data storage module, the data analysis module is connected with the data storage module, and the estimating and evaluating module is connected with the data storage module and the data analysis module.
4. A system for predicting the insulating performance of a high voltage electrical apparatus as claimed in claim 3, wherein: the data acquisition module comprises a temperature acquisition module, a humidity acquisition module, a sea wind strength acquisition module, a salt content acquisition module, a sea wave height acquisition module, a vibration frequency acquisition module and a dust concentration acquisition module, wherein the temperature acquisition module is used for acquiring the temperature of air on the sea surface, the humidity acquisition module is used for acquiring the humidity of the air on the sea surface, the sea wind strength acquisition module is used for acquiring the strength of sea wind, the salt content acquisition module comprises an offshore air salt content acquisition module and an electric equipment ambient air salt content acquisition module, the offshore air salt content acquisition module is used for acquiring the salt content of the air on the sea surface, the electric equipment ambient air salt content acquisition module is used for acquiring the salt content of the air around the electric equipment, the sea wave height acquisition module is used for acquiring the height of sea waves, the dust concentration acquisition module is used for acquiring the dust concentration around the electric equipment, and the vibration frequency acquisition module is used for acquiring the vibration frequency of a ship.
5. The system for estimating an insulation performance of a high-voltage electrical apparatus according to claim 4, wherein: the data storage module comprises a temperature storage module, a humidity storage module, a sea air strength storage module, a salt content storage module, a sea wave height storage module, a vibration frequency storage module and a dust concentration storage module, wherein the salt content storage module comprises an offshore air salt content storage module and an electrical equipment surrounding air salt content storage module, the temperature storage module is connected with the temperature acquisition module, the humidity acquisition module is connected with the humidity storage module, the sea air strength storage module is connected with the sea air salt content acquisition module, the sea air salt content storage module is connected with the offshore air salt content acquisition module, the electrical equipment surrounding air salt content storage module is connected with the electrical equipment surrounding air salt content acquisition module, the sea wave height storage module is connected with the sea wave height acquisition module, the vibration frequency storage module is connected with the vibration frequency acquisition module, the dust concentration storage module is connected with the dust concentration acquisition module, the temperature storage module, the sea air strength storage module, the salt content storage module, the sea wave height storage module, the vibration frequency storage module and the dust concentration storage module are all connected with the data analysis module, the prediction data analysis module is used for estimating the relevant data of the data analysis historical performance of the data analysis module is used for estimating the relative insulation performance of the relevant piezoelectric insulation performance, and the analysis module is used for evaluating the insulation performance of the relevant piezoelectric analysis.
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