JP2876142B2 - Oil leakage monitoring method for OF cable line - Google Patents

Description

DETAILED DESCRIPTION OF THE INVENTION (Industrial Application Field) The present invention monitors the occurrence of oil leakage in an OF cable line, and monitors the oil leakage of the OF cable line that issues an appropriate oil leakage occurrence alarm according to the situation. About the method.

(Prior Art) OF cable lines are configured to maintain predetermined withstand voltage characteristics by insulating oil filled in an insulator.

By the way, in the OF cable line, when thermal expansion and contraction occurs due to temperature change and a fatigue crack occurs in the metal sheath, oil leakage occurs, oil pressure decreases, and dielectric breakdown may occur due to void discharge. For this reason, in OF cable lines,
It is necessary to detect the occurrence of oil leaks promptly and take appropriate measures.

On the other hand, the amount of insulating oil filled in the OF cable line fluctuates due to changes in conductor temperature due to changes in load current and seasonal temperature changes, so it is easy to find small oil leaks early. is not.

Also, an alarm transmission device is attached to the refueling tank,
When the oil amount or the oil pressure is out of the specified range, an alarm is issued. The refueling tank is provided with a reserve oil amount that can be refueled to some extent even after an alarm is issued, so that emergency measures can be taken without a power outage. However, this method does not detect a small oil leak.

Therefore, various oil leakage monitoring methods for OF cable lines have been conventionally proposed.

For example, in the case of Japanese Patent Publication No. 59-8129, the amount of oil in the oil tank is estimated by calculation from the temperature around the OF cable line, and the like.
An alarm is issued by comparing the estimated oil amount with the actually measured oil amount in the oil tank.

This solves the problem that it is difficult to early detect oil amount fluctuations due to a small amount of oil leakage when the oil amount fluctuations due to conductor temperature changes and seasonal temperature changes are large.

(Problems to be Solved by the Invention) By the way, even with the above-described conventional method, based on the criteria for determining whether or not an oil leak has actually occurred and the obtained data, what degree of urgency level It is not always clear whether or not to issue an oil leak warning.

In other words, whether the state of the oil leak is necessary to immediately proceed to the repair or to the extent that the inspection interval is shortened often depends on the experience and intuition of those involved in the monitoring work, and the objectivity is poor. There was a drawback.

In addition, if the error of the estimated oil amount is large, it is impossible to perform a precise monitoring.

The present invention has been made by paying attention to the above points, is capable of early detection of a small amount of oil leakage, and determines the state of oil leakage by a relatively objective method, and determines the appropriate state according to the situation. It is an object of the present invention to provide a method for monitoring an oil leak in an OF cable line capable of issuing a warning.

(Means for Solving the Problems) The oil leak monitoring method for an OF cable line according to the present invention detects an oil amount and an oil temperature of the OF cable line, and detects an oil leak based on the oil amount and the oil temperature. A method of monitoring oil leakage in an OF cable line, wherein a first regression line indicating a relationship between an oil amount and an oil temperature based on a detection result of oil amount data and oil temperature data detected over a predetermined period of time is calculated. A first regression line calculation step for calculating;
A second regression line calculating step of calculating a second regression line indicating a relationship between the oil amount and the oil temperature based on the detection results of the oil amount data and the oil temperature data detected over a time different from the predetermined time. A first oil amount estimation value calculating step of calculating a first oil amount estimation value at a predetermined oil temperature based on the first regression line; and the predetermined oil amount calculation step based on the second regression line. A second oil amount estimation value calculating step of calculating a second oil amount estimation value at a temperature, and detecting the oil leakage by comparing the first oil amount estimation value and the second oil amount estimation value. And an oil amount estimation value comparing step.

(Operation) In the above method, in order to eliminate the effect of oil volume fluctuations due to changes in conductor temperature due to changes in load current and changes in outside air temperature,
A correlation between the oil amount and the oil temperature is statistically analyzed to obtain an estimated oil amount. When an oil leak occurs, the estimated oil amount changes over time. Therefore, by comparing the estimated oil amount values detected over different periods of time, it is possible to judge the state of occurrence of oil leakage on an objective basis. In this case, if the oil amount estimation values detected over different times are compared and the oil leakage occurrence state is determined based on the duration of the abnormal state, precise monitoring is possible.

(Examples) Hereinafter, the present invention will be described in detail with reference to the illustrated examples.

First, FIG. 1 is a functional block diagram of a system that implements the OF cable line monitoring method of the present invention.

In the figure, according to the method of the present invention, first, for example,
About three-phase OF cable line consisting of A-phase, B-phase, and C-phase,
Monitor the amount and temperature of insulating oil in each phase. That is, from each phase, the oil amount data 1a, 1b, 1c and the oil temperature detection data 1a ', 1b',
Get 1c '. Since it is difficult to directly detect the oil temperature inside the cable, a cable sheath temperature or a transmission current value for obtaining oil temperature data is accepted. Here, these are referred to as oil temperature detection data.

In order to receive these data, a monitoring unit 10 is provided, and further, a central office 20 is provided which receives an output of the monitoring unit 10 and generates an oil leak occurrence alarm or the like.

The central office 20 is a center for centrally monitoring the state of a large number of OF cable lines. The central office 20 receives output signals from a plurality of monitoring units (not shown) via a communication circuit or the like. I do.

The monitoring unit 10 includes a data processing unit 11, a regression line calculation unit 1,
2. It is composed of an oil amount estimation value calculation unit 13, an oil amount estimation value comparison unit 14, and an oil leakage occurrence state determination unit 15. These detailed operations will be described later.

FIG. 2 shows a block diagram of a system that embodies the functional blocks of FIG.

In the figure, oil cables 2A, 2B, 2C are provided in OF cables 1A, 1B, 1C constituting a three-phase OF cable line, respectively, and oil amount management is performed independently. Each of the oil tanks 2A, 2B, 2C is provided with an oil amount sensor 3A, 3B, 3C. This oil amount sensor detects the liquid level of the insulating oil contained in the oil tank, converts this into an electric signal, and outputs it to the monitoring unit 10. Various well-known sensors, such as a float type sensor and a sensor using an optical sensor, are used as the oil amount sensors.

Further, each of the OF cables 1A, 1B, 1C is provided with a cable sheath temperature sensor 4A, 4B, 4C and a power transmission ammeter 5A, 5B, 5C, respectively.

Each of the cable sheath temperature sensors 4A, 4B, 4C is composed of a thermistor or the like directly attached to the sheath of each cable, and converts the sheath temperature into an electric signal and outputs it to the monitoring unit 10. Each of the transmission ammeters 5a, 5b, and 5c includes a current transformer or the like provided in a cable head, a substation, or the like.
The transmission current is converted into an electric signal and output to the monitoring unit 10.

In the method of the present invention, the oil leakage is always monitored by focusing on the OF cable of any one phase.
Only the oil leak monitoring method for the cable 1A will be described. It goes without saying that the same monitoring is performed on the other-phase OF cables.

 The monitoring unit 10 receives various data for monitoring.

The monitoring unit 10 includes an input port 101 and an output port 102,
For a bus line 103, a processor 104, a random access memory (RAM) 105, a keyboard 106, a printer 10
7. It is composed of a device to which a read only memory (ROM) 108, a magnetic disk device 109, a display 110 and the like are connected.

The output signal of the monitoring unit 10 is connected via a communication line 30 to a computer 20a provided in the central office.

Here, the processor 104 of the monitoring unit 10 includes a microcomputer or the like that controls the operation of the entire monitoring unit. The read-only memory 108 is a memory that stores an operation control program of the processor 104 and the like.

The random access memory 105 is a memory for storing operation parameters of the processor 104 and other data.

As shown in the figure, the random access memory 105 includes an oil amount data storage unit 105a, an oil temperature calculation table storage unit 105b, an oil temperature data storage unit 105c, and a calculation determination table storage unit 105d. Is provided.

The oil amount data storage unit 105a is an area for storing a large number of oil temperature data sampled over a predetermined time input via the input port 101. Further, the oil temperature calculation table storage unit 105b includes a calculation table that calculates oil temperature data based on the oil temperature detection data received from the input port 101 and outputs the result. Oil temperature data storage unit 105c
Is an area where the oil temperature data obtained by the above calculation is sampled and stored over a predetermined time period. The calculation determination table storage unit 105d calculates an oil amount estimation value based on the oil amount data and the oil temperature data detected over the predetermined time period, and finally outputs a determination result of an oil leakage occurrence state. Consists of tables and arithmetic programs.

The keyboard 106 is provided for an operator to input a command or the like for controlling the operation of the monitoring unit 10. The printer 107 is a printing device for printing out the monitoring result of the monitoring unit 10. Magnetic disk unit 109
Is an external storage device for storing and storing oil amount detection data, monitoring results, and the like. The display 110 is composed of a display device used for guiding the operation of the monitoring unit 10 by the operator and for monitoring the monitoring result. The input port 101 and the output port 102 are composed of an interface for transmitting and receiving data and a communication control circuit.

Next, the method of the present invention will be specifically described with reference to FIG.

FIG. 3 shows that finally the central office 20
It is a specific flowchart up to the execution of a process for issuing a predetermined alarm in (FIG. 1).

FIG. 4 is a diagram for explaining the operation of the arithmetic processing performed in each step of this flowchart.

First, in step S1 of the flowchart of FIG.
From the input port 101 of the monitoring unit 10 shown in FIG.
Oil amount data and oil temperature detection data are received every hour over a week, and the oil amount data is stored in the oil temperature data storage unit 105a of the random access memory 105 as it is. The oil temperature detection data is processed by the oil temperature calculation table storage unit 105b, and is then stored in the oil temperature data storage unit 10b.
Stored in 5c. The total number of sampled data is 24 × 7 × 2, that is, 336 in two weeks (FIG. 4). In addition, the oil amount is V _ai , V phase, A phase, B phase, C phase, respectively.
_bi and V _ci , and the oil temperatures are T _ai , T _bi and T _ci . The subscript i is
The numbers are sequentially assigned in correspondence with the time at which the oil amount detection data was obtained.

Next, in step S2 in FIG. 3, the data for the two weeks is divided into data up to one week before the current time (group B) and data before that (group A). This is for observing a temporal change in the relationship between the oil temperature and the oil amount.

As described above, the processing of step S1 and step S2 is executed by the data processing unit 11 shown in FIG.

Next, the processing of steps S3 and S4 is executed, and the subsequent operations are performed all at once by the operation determination table storage unit 105d in the random access memory 105 in FIG. 2, as described above. It is possible to do. However, here, the content of the calculation will be specifically decomposed and described.

Next, a regression line between the oil amount and the oil temperature is obtained for each group (step S3).

As shown in FIG. 5, the oil temperature is plotted on the horizontal axis and the oil quantity is plotted on the vertical axis, and the correlation is plotted on a graph.

Here, as the oil temperature T, data at a time earlier than the time when the oil amount V is measured by a certain time Δt is used. this is,
Actually, even if the transmission current or the like increases or decreases, the effect on the oil amount is delayed by Δt.

Then, a regression line K is obtained based on the least squares method from the data distributed in a substantially strip shape. The equation is a straight line having a slope β as shown in FIG. Further, the slope β is obtained from the equation. A first regression line is obtained based on the data detection result of the group A, and a second regression line is obtained based on the data detection result of the group B. The above calculation processing is
This is performed by the regression line calculation unit 12 shown in FIG.

Next, an estimated oil amount value is determined from FIG. 4 using the average value of the oil amount, the slope β of the regression line, the average value of the temperature, and the confidence limit α (step S4).

Note that confidence limits α of the oil amount in the oil temperature T _x is determined by the fourth scheme.

Also, the F value indicating the probability that the true value is within the range is:
Based on a known F distribution table (statistics table of the Japan Standards Association, etc.), a value of F = 3.93 for 95% reliability and a value of F = 6.79 for 99% reliability are used.

That is, the F value increases as the reliability increases, and the range of the true oil amount estimated value at a constant oil temperature increases. Conversely, if the reliability is low, the F value becomes smaller, and the range of the true oil amount estimation value at a constant oil temperature becomes narrower.
The error variance V _e used in the equation for calculating the confidence limit is
It is obtained by the formula. Further, the error fluctuation _Se is obtained by an equation. These equations are based on known statistics.

In step S4, based on the first regression line, the average oil temperature _a of group A is used as _a reference in the manner described above.
A first oil amount estimation value is obtained, and a second oil amount estimation value is obtained based on a second regression line. That is, where _a to T _x in the formula, used to calculate the amount of oil estimate of both groups.

The above calculation process is performed by the oil amount estimation value calculation unit 13 in FIG.

Next, in consideration of the confidence limit α, it is compared whether the second oil estimated value of the group B is lower than the first oil amount estimated value of the group A (step S5).

That is, as shown in the fourth scheme, the confidence limits alpha, T _x is minimized equal to the average value of the oil temperature. Therefore, whether or not there is a difference between the amount of oil estimate based on the amount of oil estimate and the group B based on the group A, for any amount of oil it estimates the average T _x is the oil temperature data from the A group In the case where the value is equal to the value _a , the two may be compared.

FIG. 6 illustrates a first regression line based on Group A and a second regression line based on Group B. Similar to FIG. 5, this graph shows the oil temperature on the horizontal axis and the oil amount on the vertical axis.

Here, when the position of the average oil temperature _a of the group A is vertically represented by a broken line as shown in the figure, the intersection with the first regression line is the intersection with the second regression line. And the first
The range of the oil amount estimation value at the 99% reliability of the regression line is a section, and its minimum value is _A99 . Further, the range of the oil estimated value at 95% confidence level of the first regression line becomes interval, the minimum value of each is the A _95. On the other hand, when looking at the second regression line, the range of the oil amount estimation value at the 99% reliability is a section, the maximum value of which is B ₉₉ , and the range is 95%.
The range of the oil amount estimation value in the reliability is a section, and the maximum value is _B95 .

As can be seen from this graph, the fact that the second regression line of Group B, which is the latest data, is located lower than the first regression line of Group A, which is the data obtained immediately before, is lower. , Indicates that the oil amount has decreased, and the cause is presumed to be oil leakage or the like. However, the existence probability of the oil amount estimation value calculated based on the regression line has a certain width depending on the reliability limit, that is, the reliability shown here.
Therefore, the oil amount estimation value based on the first regression line and the oil amount estimation value based on the second regression line are compared at a specific oil temperature, and a range of a reliability limit at a predetermined reliability is considered. Still, if the estimated oil amount based on the second regression line is small, it is determined that oil leakage has occurred.

The above determination is made by the estimated oil amount comparing section 14 in FIG.

Finally, the oil amount generation state determination unit 15 in FIG. 1 determines what kind of alarm should be issued.

First, the above estimated oil volume values were compared with 95% reliability, and A ₉₅
If the abnormal state of> _B95 continues for three days, it is determined that the alarm level is "1" (step S6). Such a determination result is the second
Of the central office via the output port 102.

Note that the alarm level “1” indicates that the occurrence of oil leakage with a relatively low urgency is suspected, and for example, it is necessary to take measures such as strengthening the monitoring of the phase in the future.

On the other hand, the above comparison is made with a reliability of 95%. If the abnormality state of _A95 > _B95 continues for 7 days, the alarm level is set to "2" (step S7). This is considered a medium urgency spill,
A corresponding measure is taken.

If the abnormal state of A ₉₉ > B ₉₉ continues for 7 days with the reliability of 99%, the alarm level is set to “3” (step S8). The alarm level “3” is regarded as a relatively urgent oil leak.

The specific method of determining the state of occurrence of oil leakage is shown in the table of FIG.

In the figure, the occurrence of an abnormal state is indicated by a circle, and the data of the groups A and B described above are compared every day.
The comparison determination results are listed for 99% reliability and 95% reliability. Then, the alarm level is determined based on the number of consecutive circles.

Figure 7 (a) When issuing an alarm level "1" at A _95> B _95, FIG. (B) When issuing an alarm level "2" in the A _95> B _95, FIG. (C) is A ₉₉ > B The determination result when the alarm level “3” is issued at ₉₉ .

Incidentally, with such alarm above, it is preferred that the amount of oil to display the estimated time T _al to reach the alarm calling level defined in the oil tank at the same time (FIG. 3 step S9).
This oil leak amount is determined by using a formula for calculating the daily oil leak amount as shown in FIG. The expected time is obtained in the manner shown in FIG.

When the central office 20 shown in FIG. 1 receives the above-mentioned alarm, the central office 20 takes necessary measures according to the respective alarm levels.

 The present invention is not limited to the above embodiments.

The above calculations and the like may be performed in order by the processor itself executing a predetermined program, or, if necessary, the progress data of the calculations may be printed out and reported to the central office as it is. No problem.

The process of FIG. 3 is executed, for example, after accumulating one week of data. However, when the oil amount monitoring or the like is continuously performed, the process is always retroactively performed for one week and one week before. The calculation, comparison, and determination may be performed on the data of. In this case, the oil amount data and the oil temperature data may always have a constant capacity, and the oldest one may be deleted and the latest one may be sequentially updated.

In the present invention, oil amount data and oil temperature data at the time of soundness may be stored in a memory in advance, and the latest measurement result may be compared with the data. (Effect of the Invention) According to the method, the correlation between the oil amount and the oil temperature is statistically analyzed, so even if there is an oil amount difference fluctuation based on a change in the conductor temperature due to a change in the load current or a seasonal temperature change, Early detection of minute oil leakage becomes possible.
Furthermore, by comparing the time change of the estimated oil amount value, the state of oil leakage occurrence can be quantified and the urgency level can be objectively determined.

[Brief description of the drawings]

FIG. 1 is a functional block diagram of a system implementing the method of the present invention, FIG. 2 is a block diagram of a specific system implementing the method of the present invention, FIG. 3 is a flowchart explaining the method of the present invention, FIG. FIG. 4 is a diagram for explaining the operation of each step for carrying out the method of the present invention, FIG. 5 is a graph of a regression line of oil amount / oil temperature by the method of the present invention, and FIG. FIG. 7 is a graph for explanation, and FIG. 1a, 1b, 1c ... oil amount data, 1a ', 1b', 1c '... oil temperature detection data, 10 ... monitoring unit, 11 ... data processing unit, 12 ... regression line calculation unit, 13 ... … Estimated oil amount calculation unit, 14… Estimated oil amount comparison unit, 15… Oil leakage state determination unit, 20… Central office.

──────────────────────────────────────────────────続 き Continuing on the front page (72) Inventor Shuji Tatezaki 3-7-1, Ichibancho, Aoba-ku, Sendai, Miyagi Prefecture Tohoku Electric Power Co., Inc. (72) Inventor Yasumitsu Ebinuma Sakae Oda, Kawasaki-ku, Kawasaki-shi, Kanagawa Prefecture 2-1-1 1-1 Showa Electric Wire & Cable Co., Ltd. (72) Inventor Yuki Ando 2-1-1, Showa Electric Wire & Cable Co., Ltd. 2-1-1 Sakae Oda, Kawasaki-ku, Kawasaki City, Kanagawa Prefecture (72) Inventor Tamaki Mori Kawasaki-shi, Kanagawa Prefecture 2-1-1, Sakae Oda, Kawasaki-ku Inside Showa Electric Wire & Cable Co., Ltd. (56) References JP-A-61-106017 (JP, A) JP-A-1-107611 (JP, A) JP-A-1-315216 ( JP, A) JP-A-3-118713 (JP, A) JP-A-3-118714 (JP, A) JP-B-58-2536 (JP, B2) JP-B-59-8129 (JP, B2) (58) ) Surveyed fields (Int. Cl. ⁶ , (DB name) H02G 15/20-15/34

Claims (2)

(57) [Claims] 1. An oil amount and an oil temperature of an OF cable line are detected,
An oil leakage monitoring method for an OF cable line that detects an oil leak based on the oil amount and the oil temperature, wherein the oil amount is detected based on the oil amount data detected over a predetermined time and the oil temperature data detection result. A first regression line calculation step for calculating a first regression line indicating the relationship between the oil temperature and the oil temperature, based on oil amount data and oil temperature data detected over a time period different from the predetermined time period. A second regression line calculating step of calculating a second regression line indicating a relationship between the oil amount and the oil temperature; and a first regression line at a predetermined oil temperature based on the first regression line.
A first oil amount estimated value calculating step of calculating an oil amount estimated value of the second oil amount; and a second oil amount estimation of calculating a second oil amount estimated value at the predetermined oil temperature based on the second regression line. An OF cable comprising: a value calculation step; and an oil amount estimation value comparing step of detecting oil leakage by comparing the first oil amount estimation value and the second oil amount estimation value. Monitoring method for oil leaks on railway tracks. 2. An oil amount and an oil temperature of the OF cable line are detected,
An oil leakage monitoring method for an OF cable line that detects an oil leak based on the oil amount and the oil temperature, wherein the oil amount is detected based on the oil amount data detected over a predetermined time and the oil temperature data detection result. A first regression line calculation step for calculating a first regression line indicating the relationship between the oil temperature and the oil temperature, based on oil amount data and oil temperature data detected over a time period different from the predetermined time period. A second regression line calculating step of calculating a second regression line indicating a relationship between the oil amount and the oil temperature; and a first regression line at a predetermined oil temperature based on the first regression line.
A first oil amount estimated value calculating step of calculating an oil amount estimated value of the second oil amount; and a second oil amount estimation of calculating a second oil amount estimated value at the predetermined oil temperature based on the second regression line. A value calculating step, an oil amount estimated value comparing step of detecting oil leakage by comparing the first oil amount estimated value and the second oil amount estimated value, and a second oil amount estimated value. An oil leak occurrence state determining step of determining an oil leak occurrence state from the duration thereof when the reliability limit is lower than the reliability limit of the first oil amount estimation value. Oil leak monitoring method.