CN115713236A - Power distribution network lightning damage risk assessment method based on lightning stroke data space autocorrelation analysis - Google Patents
Power distribution network lightning damage risk assessment method based on lightning stroke data space autocorrelation analysis Download PDFInfo
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Abstract
The invention provides a power distribution network lightning damage risk assessment method based on lightning stroke data space autocorrelation analysis, which comprises the following steps: counting the continuous lightning ground flash point area; counting a thunder damage hot spot area; calculating the average lightning withstand level of the line; judging whether the line to be evaluated intersects with a continuous lightning ground flash point area or not, judging whether the line to be evaluated intersects with a lightning damage hot point area or not, sequencing the average lightning withstand levels of all the lines to be evaluated, and judging whether the average lightning withstand level of the lines is lower than a negative standard deviation or not by using a standard deviation screening method; and judging the lightning damage risk level of the power line according to the obtained judgment result. According to the method, the lightning damage risk level of the distribution line is predicted by analyzing various influence factors such as the lightning density of the ground near the distribution line and historical lightning damage faults, and the spatial repeated aggregation characteristics of the distribution network lightning damage are extracted by spatial autocorrelation analysis of the lightning density of the ground for many years, so that guidance is provided for highly-targeted differentiated lightning protection measures.
Description
Technical Field
The invention relates to the technical field of lightning protection of power systems, in particular to a power distribution network lightning damage risk assessment method based on lightning stroke data space autocorrelation analysis.
Background
The insulation level of the power distribution network is low, and the power distribution network is easily influenced by lightning overvoltage. Meanwhile, the medium and low voltage overhead distribution line has the basic characteristics of short length and small tower span, and the lightning positioning system has insufficient data precision and is difficult to accurately position the distribution network tower subjected to lightning stroke. In addition, parameters such as lightning parameters, tower structures and lightning resistance levels of the existing distribution lines are seriously homogenized, so that the error of performing lightning damage differentiation risk assessment on each base tower of the distribution lines is large by adopting the existing power transmission line differentiation lightning protection method, and the pertinence of the distribution network lightning protection measures is not strong.
The reference patents are as follows:
a differentiated lightning protection method for a power transmission line is CN201310112586.6[ P ].2013-08-07.
A differentiated lightning protection method for power transmission and distribution lines and a system thereof are CN201310060830.9[ P ].2013-06-05.
A transmission line lightning strike risk assessment method and system based on distribution network lightning strike data is CN202210777805.1[ P ].2022-08-05.
CN201710304501.2[ P ].2017-08-18 is a distribution line thunder damage risk assessment method.
The technical schemes of the existing related patents mainly have two categories: the first type is a differentiated lightning protection technology of the power transmission line, which is a technology for evaluating the lightning risk difference of the power transmission line towers by performing fine analysis on lightning parameters, tower structures and lightning resistance levels of each base line tower and implementing targeted lightning protection transformation according to the lightning risk difference. However, limited by the existing lightning positioning precision, the homogeneity of lightning parameters, tower structures and lightning-resistant horizontal parameters of the power distribution line tower is serious, and effective risk assessment of lightning damage is difficult to develop by applying the differentiated lightning protection technology of the power transmission line.
The second type of patent is a distribution line lightning damage risk assessment method, which takes the characteristics and main influence factors of the lightning damage of the distribution line into consideration. However, the method has the defects that no quantitative calculation method is provided for each influencing factor, and a unified evaluation model is lacked. Although the method can evaluate the high lightning damage risk section of a certain distribution line, the lightning damage risks of different lines in the power distribution network cannot be compared, and the effective ranking of the lightning damage risks of the power grid lines cannot be carried out, so that the method is not beneficial to the aim of comprehensive management of the power grid lightning damage.
Disclosure of Invention
The invention aims to provide a power distribution network lightning damage risk assessment method based on lightning stroke data spatial self-correlation analysis.
A power distribution network lightning damage risk assessment method based on lightning stroke data space autocorrelation analysis comprises the following steps:
step one, counting a continuous lightning ground flash point area;
step two, counting the hot spot areas of the thunder damage;
step three, calculating the average lightning resistance level of the line;
step four, judging whether the line to be evaluated intersects with the continuous lightning ground flash point area obtained in the step one, judging whether the line to be evaluated intersects with the lightning damage hot point area obtained in the step two, sequencing the average lightning withstand level of all the lines to be evaluated obtained in the step three, and judging whether the average lightning withstand level of the lines is lower than a negative standard deviation by using a standard deviation screening method;
and step five, judging the lightning damage risk level of the power line according to the judgment result obtained in the step four.
Further, the first step specifically includes: firstly, through thunder and lightning positioning system data, annual ground lightning density distribution of an area where a power line to be evaluated is located is counted, hot spot analysis is carried out on the ground lightning density distribution data of each year by adopting a Getis-Ord Gi spatial statistical algorithm, annual ground lightning hot point data is obtained, the change trend prediction of the ground lightning hot point is carried out on the ground lightning hot point data of a specific geographic position by adopting a Mann-Kendall trend test method, the spatial repeated aggregation characteristic and the regional range of historical ground lightning hot points are obtained, and a continuous thunder and lightning ground flash point region is obtained.
Further, for the ground flash density distribution data of each year, performing hotspot analysis by adopting a Getis-Ord Gi spatial statistical algorithm to obtain the ground flash hotspot data of each year, which specifically comprises the following steps: the Getis-Ord local statistic is expressed as:
wherein x j Is the attribute value of element j, ω i,j The space weight between the elements i and j is constructed by adopting a K-nearest neighbor method, and n is the total number of the elements and meets the following requirements:
and calculating the hot spot index Gi of each element in the ground flash density spatial distribution matrix, and screening the ground flash hot spot areas with Gi being more than or equal to 1 to obtain the ground flash hot spot data every year.
Further, a Mann-Kendall trend test method is adopted to predict the change trend of the ground flash point, and the space repeated aggregation characteristics and the area range of the historical ground flash point are obtained, so that the continuous lightning ground flash point area is obtained, and the method specifically comprises the following steps:
for time series X, the statistics of the Mann-Kendall trend test are as follows:
wherein x is j J is the j data value of the time sequence; n is the length of the data sample; sgn is a sign function defined as follows:
when n is larger than or equal to 8, the statistic S approximately follows normal distribution, and the mean value of the statistic S is 0;
the indexes for measuring the trend size are as follows:
in the formula, 1-straw-j-straw-i-straw, positive beta value represents an "ascending trend", negative beta value represents a "descending trend", and a continuous flash point area with beta being more than or equal to 0 is screened to obtain spatial repeated aggregation characteristics of historical flash points and area ranges thereof.
Further, the second step specifically includes: and (3) counting the density value of the lightning damage according to the historical lightning damage point position data of the power line to be evaluated, and performing hotspot analysis by adopting a Getis-Ord Gi spatial statistical algorithm to obtain the historical lightning damage hotspot area range.
Further, a Getis-Ord Gi spatial statistical algorithm is adopted to perform hotspot analysis, and a historical thunder damage hotspot area range is obtained, wherein the method specifically comprises the following steps: and calculating the hot spot index Gi of each element in the lightning density spatial distribution matrix, and screening the lightning hot spot area with Gi being more than or equal to 1 to obtain historical lightning hot spot data.
Further, the third step specifically includes: for a tower without a lightning arrester, calculating the lightning resistance level of the tower by adopting the line insulation strength; for a pole tower provided with a lightning arrester, calculating the lightning-resistant level of the pole tower by adopting the through-current capacity of the lightning arrester; and finally, carrying out arithmetic average on the lightning-resistant horizontal values of all towers in the same line to obtain the average lightning-resistant level of the line to be evaluated.
Further, for a tower which is not provided with the lightning arrester, the lightning-resistant level of the tower is calculated by adopting the line insulation strength, and the specific calculation formula is as follows:
in the formula, I is the lightning-resistant level (kA) of the tower; u is insulator insulation strength (kV); z is the line wave impedance (omega);
for a pole tower provided with a lightning arrester, the lightning-resistant level of the pole tower is calculated by adopting the through-current capacity of the lightning arrester, and the specific calculation formula is as follows:
I＝I pk
in the formula, I is the lightning-resistant level (kA) of the tower; I.C. A pk The through-current capacity of the line arrester.
Further, the fourth step specifically includes: judging whether the line to be evaluated is intersected with a continuous ground flash point region, if so, recording A =1, otherwise, recording A =0; judging whether the line to be evaluated is intersected with the thunder damage hot spot area, if so, recording B =1 in a space intersection, and otherwise, recording B =0; the average lightning withstand level of all the lines to be evaluated is ranked from high to low, and lines with an average lightning withstand level lower than a negative standard deviation are marked with C =1 and the remaining 80% of the lines are marked with C =0 by using a standard deviation screening method.
Further, the fifth step specifically includes: judging the lightning damage risk level of the power line according to the A, B and C values obtained by the fourth step: when A + B + C =3, the line has high lightning damage risk, the high risk area is located in the intersection area, and the lightning resistance level of the line is improved aiming at the ground flash hot spot area and the lightning damage hot spot area by differential lightning protection reconstruction; when A + B + C =2, the line has a medium lightning damage risk, the intersection area still has a high lightning damage risk, and the differentiated lightning protection modification aims at the pole tower positioned in the hot spot area to improve the lightning resistance level of the line; when A + B + C =1, the line has low lightning risk, and lightning protection reconstruction can be temporarily omitted to wait for re-evaluating the lightning risk in the next period.
The invention provides a power distribution network lightning damage risk assessment method mainly based on lightning stroke data space autocorrelation analysis, which comprises the following steps: firstly, the lightning location system is used to detect data and extract the area of the ground flash frequency heating point, as can be seen from fig. 4, the ground flash density has a strong correlation with the lightning damage point of the line, so that the line in the area of the ground flash frequency heating point has a high risk of lightning damage. Secondly, a line lightning damage hot spot area is extracted by utilizing the power distribution network lightning damage data, as can be seen from fig. 3, the line lightning damage presents a certain spatial gathering trend, and the risk of the line lightning damage in the lightning damage hot spot area is larger. Finally, the lightning withstand level of a line determines the trip rate of the line after being struck by lightning, so that the risk of lightning damage to a line with a low lightning withstand level in the same area is greater.
In conclusion, according to the method, the lightning parameters, the tower structure and the lightning resistance level of each base line tower are not required to be subjected to fine analysis, the lightning damage risks of the distribution lines can be sequenced, the lightning protection transformation is carried out on the lines with the highest lightning damage risks, and the lightning trip-out rate of the distribution network can be effectively reduced.
Drawings
FIG. 1 is a flow chart of a power distribution network lightning damage risk assessment method based on lightning stroke data spatial autocorrelation analysis according to an embodiment of the invention;
FIG. 2 is a rule for determining the level of the lightning damage risk of the power distribution network according to the embodiment of the invention;
FIG. 3 is a fault point of lightning damage occurring on a line of 2012;
fig. 4 is the spatial autocorrelation of the lightning fault point of the line in 2012 with the hot spot area of the ground flash density.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are some, but not all, embodiments of the present invention. All other embodiments, which can be obtained by a person skilled in the art without making any creative effort based on the embodiments in the present invention, belong to the protection scope of the present invention.
The failure mechanisms caused by lightning striking the line are mainly of two types: the first lightning directly hits a line tower or a wire to generate direct lightning overvoltage; secondly, when the ground near the line is struck by lightning, the strong electromagnetic pulse generated by lightning current can also generate harmful induced lightning overvoltage on the overhead line, and the safety of electrical equipment is damaged.
The line lightning trip-out rate is a main index for measuring the quality of the lightning protection performance of the transmission line, and the line lightning trip-out rate is calculated by referring to the standards of GB/T50064-2014 and the like, when the lightning conductor is not arranged on the distribution line, the line lightning trip-out rate is calculated by the following formula:
n＝NPη (1)
wherein n is the lightning trip-out rate of the line and has the unit of times/(hundred kilometers per year); n is the number of line ground flashes; p is the probability of causing impact flashover when the lightning current amplitude exceeds a lightning resistance level I; eta is the arc establishing rate after the insulation flashover;
the IEEE standard synthesizes the actual measurement data of lightning current all over the world, and recommends that the cumulative probability distribution of the first lightning strike-back current amplitude obeys the formula:
by analyzing the lightning damage of the overhead distribution line, the invention summarizes 3 main characteristics: (1) the lightning damage risk is inversely related to the lightning withstand level of the line, and the lower the lightning withstand level of the line is, the higher the lightning trip-out rate is in formula (1). (2) The distribution line is low in direct lightning strike probability and mainly influenced by induced overvoltage of lightning strikes nearby the line, so that the distribution line is sensitive to peripheral lightning strike frequency (the larger the number N of lightning strikes of the line is, the more frequent the lightning activity is, and the higher the lightning strike trip-out rate of the line is); as can be seen from fig. 4, the ground flash density has a strong correlation with the lightning point of the line, so that the line in the area of the ground flash point has a higher risk of lightning damage. (3) Distribution lines with serious lightning damage and disaster areas thereof are distributed more fixedly (other lightning damage influence factors can be considered through historical statistical data of the lines), such as line sections in mountainous areas or hilly areas; as can be seen from fig. 3, the line lightning damage presents a certain spatial aggregation trend, and the risk of line lightning damage in the hot spot area of the lightning damage is greater. Therefore, the risk assessment work of the lightning damage of the power distribution network can be efficiently carried out by counting the 3 indexes.
Referring to fig. 1, an embodiment of the present invention provides a power distribution network lightning damage risk assessment method based on spatial autocorrelation analysis of lightning strike data, including the following steps:
(1) and (3) counting lightning ground flash point areas:
firstly, collecting thunder and lightning positioning system data of an area where an electric power line to be evaluated is located, screening the information of the occurrence time and the position of thunder and lightning, and counting the annual lightning density distribution of the line area by using a grid method (5 km multiplied by 5 km) to obtain an annual lightning density spatial distribution matrix.
And (3) carrying out hotspot analysis on the distribution data of the lightning density of each year by adopting a Getis-Ord Gi space statistical algorithm, wherein the Getis-Ord local statistics can be expressed as:
wherein x j Is the attribute value of element j, ω i,j The space weight between the elements i and j (constructed by adopting a K-nearest neighbor method) is adopted, n is the total number of the elements and satisfies the following conditions:
and calculating the hot spot index Gi of each element in the ground flash density spatial distribution matrix, and screening the ground flash hot spot regions with Gi being more than or equal to 1 to obtain the annual ground flash hot spot data.
And predicting the change trend of the ground flash point data of the specific geographic position by adopting a Mann-Kendall trend test method. For time series X, the statistics of the Mann-Kendall trend test are as follows:
wherein x is i Is the ith data value, x, of the time series j Is the jth data value;
n is the length of the data sample;
sgn is a sign function defined as follows:
when n ≧ 8, the statistic S roughly follows a normal distribution with a mean of 0.
The indexes for measuring the trend size are as follows:
in the formula, 1< -j < -i < -n >, positive beta values indicate an "upward tendency", and negative beta values indicate a "downward tendency". Screening the continuous flash point region with beta being more than or equal to 0 to obtain the spatial repeated aggregation characteristics of historical flash points and the region range thereof.
And judging whether the line to be evaluated intersects with the continuous ground flash point region, if so, recording A =1, otherwise, recording A =0.
(2) The statistical method of the thunder damage hot spot area comprises the following steps:
firstly, collecting the historical lightning damage point position data of the power line to be evaluated, and using a grid method (5 km multiplied by 5 km) to count (all) the lightning damage density distribution to obtain a line lightning damage density spatial distribution matrix.
And performing hotspot analysis on the lightning damage density distribution data by adopting a Getis-Ord Gi spatial statistical algorithm. And calculating the hot spot index Gi of each element in the lightning damage density spatial distribution matrix, and screening a lightning damage hot spot area with Gi being more than or equal to 1 to obtain historical lightning damage hot spot data.
And judging whether the line to be evaluated is intersected with the historical thunder and lightning hot spot area, if so, recording B =1 in a space intersection, and otherwise, recording B =0.
(3) The average lightning resistance level calculation method of the line comprises the following steps:
for the tower without the lightning arrester, calculating the lightning-resistant level of the tower by adopting the line insulation strength:
in the formula, I is the lightning-resistant level (kA) of the tower; u is insulator dielectric strength (kV); z is the line wave impedance (Ω).
For the pole tower provided with the lightning arrester, calculating the lightning-resistant level of the pole tower by adopting the through-flow capacity of the lightning arrester:
I＝I pk
in the formula, I is the lightning-resistant level (kA) of the tower; i is pk The through-current capacity of the line arrester.
And finally, carrying out arithmetic average on the lightning-resistant level values of all the towers in the same line to obtain the average lightning-resistant level of the line. The average lightning strike-through levels of all lines to be evaluated are ranked (from high to low), and lines with average lightning strike-through levels low and one negative standard deviation are scored as C =1 and the remaining lines are scored as C =0 using standard deviation screening.
(4) And (3) judging the lightning damage risk level of the power line through the A, B and C values:
as shown in fig. 2, when a + B + C =3, the line has a high lightning risk, and the high risk area is located in the intersection area, and the differentiated lightning protection improvement should improve the lightning resistance level of the line for the ground flash hot spot area and the lightning hot spot area; when A + B + C =2, the line has a medium lightning risk, the intersection area still has a high lightning risk, and the differentiated lightning protection reconstruction aims at the tower located in the hot spot area to improve the lightning resistance level of the line; when A + B + C =1, the line has low lightning risk, and lightning protection reconstruction can be temporarily omitted to wait for re-evaluating the lightning risk in the next period.
The above description is only for the specific embodiments of the present invention, but the scope of the present invention is not limited thereto, and any changes or substitutions that can be easily conceived by those skilled in the art within the technical scope of the present invention are also within the scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.
Claims (10)
1. A power distribution network lightning damage risk assessment method based on lightning stroke data space autocorrelation analysis is characterized by comprising the following steps: the method comprises the following steps:
step one, counting a continuous lightning ground flash point area;
step two, counting the hot spot areas of the thunder damage;
step three, calculating the average lightning resistance level of the line;
step four, judging whether the line to be evaluated intersects with the continuous lightning ground flash point area obtained in the step one, judging whether the line to be evaluated intersects with the lightning damage hot point area obtained in the step two, sequencing the average lightning withstand level of all the lines to be evaluated obtained in the step three, and judging whether the average lightning withstand level of the lines is lower than a negative standard deviation by using a standard deviation screening method;
and step five, judging the lightning damage risk level of the power line according to the judgment result obtained in the step four.
2. The power distribution network lightning damage risk assessment method based on lightning stroke data spatial autocorrelation analysis according to claim 1, characterized in that: the first step specifically comprises: firstly, calculating annual ground flash density distribution of a region where a power line to be evaluated is located through thunder and lightning positioning system data, performing hotspot analysis on the ground flash density distribution data of each year by adopting a Getis-Ord Gi star spatial statistical algorithm to obtain annual ground flash point data, predicting the change trend of the ground flash point data of a specific geographic position by adopting a Mann-Kendall trend test method to obtain spatial repeated aggregation characteristics and a region range of historical ground flash points, and thus obtaining a continuous thunder and lightning ground flash point region.
3. The power distribution network lightning damage risk assessment method based on lightning stroke data spatial autocorrelation analysis according to claim 2, characterized in that: the method comprises the following steps of carrying out hotspot analysis on the distribution data of the ground flash density of each year by adopting a Getis-Ord Gi space statistical algorithm to obtain the data of the ground flash hotspot of each year, wherein the method specifically comprises the following steps: the Getis-Ord local statistic is expressed as:
wherein x is j Is the attribute value of element j, ω i,j The space weight between the elements i and j is constructed by adopting a K-nearest neighbor method, and n is the total number of the elements and meets the following requirements:
and calculating the hot spot index Gi of each element in the ground flash density spatial distribution matrix, and screening the ground flash hot spot regions with Gi being more than or equal to 1 to obtain the annual ground flash hot spot data.
4. The power distribution network lightning damage risk assessment method based on lightning stroke data spatial autocorrelation analysis as claimed in claim 3, characterized in that: the method comprises the following steps of predicting the change trend of the ground flash point by adopting a Mann-Kendall trend inspection method to obtain the space repeated aggregation characteristics and the area range of the historical ground flash point, and obtaining the continuous lightning ground flash point area, wherein the method specifically comprises the following steps of:
for time series X, the statistics of the Mann-Kendall trend test are as follows:
wherein x is j J is the j data value of the time sequence; n is the length of the data sample; sgn is a sign function defined as follows:
when n is more than or equal to 8, the statistic S approximately follows normal distribution, and the mean value of the statistic S is 0;
the indexes for measuring the trend size are as follows:
in the formula, 1-straw-j-straw-i-straw, positive beta value represents an "ascending trend", negative beta value represents a "descending trend", and a continuous flash point area with beta being more than or equal to 0 is screened to obtain spatial repeated aggregation characteristics of historical flash points and area ranges thereof.
5. The power distribution network lightning damage risk assessment method based on lightning stroke data spatial autocorrelation analysis according to claim 1, characterized in that: the second step specifically comprises: and (3) counting the density value of the lightning damage according to the historical lightning damage point position data of the power line to be evaluated, and performing hotspot analysis by adopting a Getis-Ord Gi spatial statistical algorithm to obtain the historical lightning damage hotspot area range.
6. The power distribution network lightning damage risk assessment method based on lightning stroke data spatial autocorrelation analysis according to claim 5, characterized in that: the method comprises the following steps of performing hotspot analysis by adopting a Getis-Ord Gi spatial statistical algorithm to obtain a historical thunder damage hotspot region range, wherein the method specifically comprises the following steps: and calculating the hot spot index Gi of each element in the lightning density spatial distribution matrix, and screening the lightning hot spot area with Gi being more than or equal to 1 to obtain historical lightning hot spot data.
7. The power distribution network lightning damage risk assessment method based on lightning stroke data spatial autocorrelation analysis according to claim 1, characterized in that: the third step specifically comprises: for a tower without a lightning arrester, calculating the lightning resistance level of the tower by adopting the line insulation strength; for a tower provided with the lightning arrester, calculating the lightning-resistant level of the tower by adopting the through-flow capacity of the lightning arrester; and finally, carrying out arithmetic average on the lightning-resistant level values of all the towers in the same line to obtain the average lightning-resistant level of the line to be evaluated.
8. The power distribution network lightning damage risk assessment method based on lightning stroke data spatial autocorrelation analysis according to claim 7, characterized in that: wherein to the shaft tower of not installing the arrester, adopt line dielectric strength to calculate the resistant thunder level of shaft tower, specific computational formula is:
in the formula, I is the lightning-resistant level (kA) of the tower; u is insulator dielectric strength (kV); z is the line wave impedance (omega);
for a tower provided with an arrester, the lightning withstand level of the tower is calculated by adopting the through-flow capacity of the arrester, and the specific calculation formula is as follows:
I＝I pk
in the formula, I is the lightning-resistant level (kA) of the tower; i is pk The through-current capacity of the line arrester.
9. The power distribution network lightning damage risk assessment method based on lightning stroke data spatial autocorrelation analysis according to claim 1, characterized in that: the fourth step specifically comprises: judging whether the line to be evaluated is intersected with a continuous ground flash point region, if so, recording A =1, otherwise, recording A =0; judging whether the line to be evaluated is intersected with the thunder damage hot spot area, if so, recording B =1 in a space intersection, and otherwise, recording B =0; the average lightning withstand level of all lines to be evaluated is ranked from high to low, and using standard deviation screening, lines with an average lightning withstand level below one negative standard deviation are scored as C =1, and the remaining 80% are scored as C =0.
10. The power distribution network lightning damage risk assessment method based on lightning stroke data spatial autocorrelation analysis according to claim 9, characterized in that: the fifth step specifically comprises: judging the lightning damage risk level of the power line according to the A, B and C values obtained by the fourth step: when A + B + C =3, the line has high lightning damage risk, the high risk area is located in the intersection area, and the lightning resistance level of the line is improved aiming at the ground flash hot spot area and the lightning damage hot spot area by differential lightning protection reconstruction; when A + B + C =2, the line has a medium lightning risk, the intersection area still has a high lightning risk, and the differentiated lightning protection reconstruction aims at the tower located in the hot spot area to improve the lightning resistance level of the line; when A + B + C =1, the line has low lightning risk, and lightning protection reconstruction can be temporarily omitted to wait for re-evaluating the lightning risk in the next period.
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| CN117706280B (en) * | 2024-02-05 | 2024-06-04 | 南昌科晨电力试验研究有限公司 | Distribution line lightning fault positioning method and system based on multi-source data |
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