JP4863918B2 - OF cable abnormality detection device and oil leakage detection method of OF cable - Google Patents

Description

  The present invention relates to a technique for monitoring the oil amount, gas pressure, and oil pressure of an OF (Oil Filled) cable, and more particularly, an OF cable abnormality detection device and an OF cable that can detect signs of abnormality at an early stage before an accident occurs. The present invention relates to an abnormality detection method.

  In general, detection of abnormal oil leakage due to the occurrence of damage to the OF cable or slight cracks in the metal sheath due to metal fatigue, etc. is performed by measuring the oil amount, hydraulic pressure, gas pressure, outside air temperature, etc. through periodic inspections. The trend has been done by comparing the trend with past records, or by providing a monitoring means for oil pressure drop in the oil tank and receiving an alarm.

  For example, in Patent Document 1, in an oil leakage determination method for an OF cable that determines oil leakage from a laid OF cable, the amount of oil in the oil tank of the OF cable and the change in oil pressure in a state where no oil has leaked Is measured and stored at appropriate intervals, and the current of the OF cable and the current of other cables installed are measured and stored, and this oil amount, oil pressure and current are used to approximate the oil amount or oil pressure. A function is obtained, and the difference between the oil quantity or hydraulic pressure value of the current oil tank and the oil quantity or hydraulic pressure value obtained from the approximate statistical function at the time of no oil leakage is obtained, and the increase of the obtained difference occurs. A method for determining oil leakage is described.

Patent Document 2 further describes a fixed method for determining an oil leak by obtaining an approximate statistical function of the oil amount or hydraulic pressure using the outside air temperature and the soil temperature.
By the way, oil leakage accidents are generally classified into oil leakages that occur due to a decrease in hydraulic pressure and parts deterioration associated with electrical accidents. The former is characterized by a large amount of oil leakage, and the latter oil is small. In addition, air temperature and underground temperature exist in the specification of the refueling setting calculation, but the conventional method manages these temperatures and measured values, and determines the oil leakage by the correlation calculation. Even in the case, the measurement value varies. In recent years, as the concept of oil leakage in oil-filled facilities has changed from priority to electric lines to priority in the environment, it is possible to determine abnormalities for multi-leakage oil with the conventional method, but it is not possible to determine minute oil leaks. There was a risk of discovery being delayed.
JP-A-6-105444 JP-A-6-335148

  The present invention has been made in view of the above-described circumstances, and more accurately detects not only a large amount of oil leakage but also a minute amount of oil leakage, and the oil before the occurrence of an alarm, which is a design alarm point. An object of the present invention is to provide an OF cable abnormality detecting device and an OF cable abnormality detecting method capable of detecting an abnormality at an early stage from the change state of the amount or the hydraulic pressure / gas pressure.

  In order to achieve the above object, the OF cable abnormality detection device according to the present invention calculates a prediction equation by multiple regression analysis using an oil amount, oil pressure, or gas pressure as a target variable, and one or more temperature types as explanatory variables, An OF cable abnormality detection device that monitors an abnormality of an OF cable by using this prediction formula, the means for inputting the measured values of the target variable and the explanatory variable, and the measured values sequentially shifted in time for each temperature type A correlation variable calculating means for calculating a target variable using the calculation result, calculating a correlation with the measured value of the target variable and calculating an optimum time from the calculation result, and a time before the time calculated by the correlation calculation means for each temperature type A determination means for determining a sign of abnormality based on a difference between a predicted value of a target variable calculated based on the prediction formula using the measurement data of the measurement data and a measured value of the target variable. And features.

  In the present invention, the objective variable is sequentially calculated by using the measured values shifted in time by the correlation calculation means, and the time having the largest correlation with the actually measured value is set as the shift time of the temperature type. Then, a prediction calculation is performed using the measurement data before the shift time to detect an abnormality sign.

  Here, “time” is intended to include not only hours, minutes and seconds but also days and months. Further, the shift time may be changed depending on the season.

  Further, the OF cable abnormality detection method according to the present invention calculates a prediction formula by multiple regression analysis using the oil amount, hydraulic pressure, or gas pressure as a target variable, and one or more temperature types as explanatory variables, and this prediction formula A method for detecting an abnormality in an OF cable using a step of inputting a measured value of the target variable and the explanatory variable, and calculating a target variable using the measured values sequentially shifted in time for each temperature type A correlation calculation step of calculating a correlation with the measurement value of the target variable and calculating an optimum time from the calculation result, and using the measurement data before the time obtained by the correlation calculation step for each temperature type And determining a sign of abnormality based on a difference between a predicted value of the target variable calculated based on the prediction formula and a measured value of the target variable.

  More specifically, when the target variable is an oil amount, the temperature type is an indoor air temperature such as a terminal junction box or an oil supply pipe, an on-ground ground temperature such as a cable / intermediate junction box, and the seabed. Including the underground temperature, the indoor air temperature uses the current temperature, the land underground temperature uses the average temperature of the previous day, and the seabed underground temperature uses the sea water temperature of the previous day.

Further, when the target variable is gas pressure or hydraulic pressure, the temperature type is the indoor air temperature such as the terminal junction box or the oil supply pipe, the ground temperature of the land such as the cable / intermediate junction box, and the seabed The indoor air temperature includes the current temperature, the land underground temperature uses the average temperature of the previous day, and the sea floor underground temperature uses the current sea water temperature.
As a result, it becomes possible to monitor with higher accuracy than the method of measuring using the current temperature.

  According to the present invention, an abnormality determination criterion is set, and first, by using multiple regression analysis, it is determined by using multiple regression analysis which measurement temperature is optimally used from the past to the present to determine abnormality at the current time point. Since the abnormality is determined using the measurement value at the optimum time, it is possible to determine the minute oil leakage with high accuracy, and thus it is possible to take an early response and treatment.

  Embodiments of the present invention will be described below. FIG. 1 is a functional block diagram of an OF cable abnormality detection device 1 according to the present embodiment. Here, the OF cable abnormality detection device 1 includes an input unit 2 for inputting various calculation parameters, monitoring data, and the like, a storage unit 30 for storing the input data, a calculation unit 10 for performing calculation for abnormality detection, and An output unit 3 is provided for outputting a warning message or the like when an abnormality is detected. The input unit 2 may be a keyboard or mouse of a general-purpose computer and may be input by an operator, or may be a device that automatically collects remote data by connecting to a communication network.

  In addition, the calculation unit 10 performs the multiple regression analysis by shifting the time (including day and month) of the input temperature data by storing the input data in the storage unit 30, the input processing means 11, and has the largest correlation. Correlation calculating means 12 for calculating the shift time, predicted value calculating means 13 for calculating the predicted value of the target variable using the past measured value for the shift time calculated by the correlation calculating means 12, and the calculated predicted value and measured value And an abnormality determining means 14 for determining the presence or absence of an abnormality sign, and an alarm output means 15 for outputting an alarm when it is determined that there is an abnormality sign as a result of this determination.

  Each means 11-15 of the calculating part 10 is a function realizable by a program as a function of a computer system.

  The storage unit 30 also stores an input data file 31 for storing input data, a shift time file 32 for storing the shift time obtained by the correlation calculation means 12 for each temperature type, and a calculation result of the predicted value. An operation result file 33 is included.

<1. Data input processing>
The OF cable abnormality detection device 1 inputs data to be monitored via the input unit 2. The input data is stored in the input data file 31 of the storage unit 30 by the input processing means 11 of the calculation unit 10.

  FIG. 2 is a data configuration example of the input data file 31. Here, the input data file 31 includes time information for each temperature type, such as a local thermometer, JMA data, and seawater temperature data, and stores measurement values. Note that the input data may include data other than the data shown in FIG.

<2. Prediction formula processing>
(Shift time calculation processing)
The correlation calculation means 12 uses each temperature type of the input data file 31 as an explanatory variable, and calculates an approximate expression of the target variable using data of a predetermined time before the explanatory variable. The predetermined time may be calculated by the operator in advance by setting time (may be day or month) candidates and using the data before the set time. Based on the data stored in the data file 31, current or past data may be automatically extracted and calculated.

  For example, as shown in FIG. 3, the calculation calculates the correlation for each temperature type and for each shift time, determines the shift time having the largest correlation for each temperature type, and shift time file illustrated in FIG. Save to.

  Next, the correlation calculation means 12 sets a prediction formula according to the processing procedure shown in FIG. First, data such as oil amount / hydraulic pressure, outside air temperature (local temperature, weather station announcement value), tidal current value, weather station announcement average temperature, seawater temperature, and the like are captured (S101). At this time, referring to the shift time file 32 of FIG. 4, data for a predetermined time is extracted for each explanatory variable (S102). Then, an approximate statistical function (prediction formula) is set by multiple regression analysis (S103).

(Example)
In the specification of the OF cable refueling setting, there are generally two types of air temperatures: the air temperature (outside air temperature) of the terminal connection box and the oil supply pipe and the like, and the underground temperature of the cable and the intermediate connection box.

  However, in this embodiment, considering the application to the OF cable passing through the seabed and the land conduit as in the configuration of the track outline diagram shown in FIG. The temperature type shown in FIG. 7 was used as the explanatory variable.

  Depending on the installation environment of the OF cable, the underground temperature and the temperature in the manhole (MH) may be used as explanatory variables.

  Also, for cables, intermediate junction boxes, etc. (landside), the daily average temperature of the Japan Meteorological Agency is used, but the measurement date, the previous day, 2 days ago, 3 days ago, 4 days ago, 5 days ago, 6 days ago, 1 week (7 days) The data with the highest correlation among the data of the previous month, one month ago, and two months ago was used.

  On the other hand, the measurement date, the previous day, 2 days, 3 days, 4 days, 5 days, 6 days, 1 week (7 days), 1 month ago, 2 The most correlated sea water temperature data was used among the data from the previous month.

  Next, the correlation is calculated for each shift time described above, the shift time with the highest correlation is determined, and the coefficient b1, b2, b3, b0 of the following prediction formula is obtained by multiple regression analysis using the temperature data. Calculate

y ′ = b1 · x1 + b2 · x2 + b3 · x3 + b0 (1)
Here, y ′ is an objective variable, and x1, x2, and x3 are explanatory variables.

  Further, y ′ is a reference value of the oil amount, gas pressure, or hydraulic pressure, x1 is the temperature measured by the local thermometer, and x2 is the JMA data at the time difference having the highest correlation in the correlation calculation stage. Further, x3 is the seawater temperature at the time difference having the highest correlation in the correlation calculation stage.

  In general, the time difference is different for each objective variable. The relationship between the objective variable (y ′) and the target variables (x1, x2, x3) is as shown in FIG.

  The step of calculating the recent statistical function may be performed by the OF cable abnormality detecting device 1, but the parameters of the approximate statistical function obtained by processing by a general-purpose computer are obtained. You may make it take in.

(Abnormality judgment processing)
Next, an oil leakage / gas leakage sign determination process will be described with reference to FIG. First, the predicted value calculation means 32 calculates the predicted value of the current target variable using the prediction formula obtained in the advance preparation stage and the past data for the shift time (S201, S202). Then, the abnormality determination means 14 compares the measured data of the oil amount / hydraulic pressure, which is the target variable, with the predicted value (S203), and a difference of a predetermined value or more occurs a predetermined number of times (“YES” in S204). If the difference is widened (“YES” in S205), an alarm is output through the alarm output means 15 because there is an indication of oil leakage / gas leakage (S206). Further, after the processing of step S202, the difference between the actual value and the predicted value of the target variable is displayed in a graph on the output unit 3 (S207), and an alarm output (S206) is output by an external abnormality determination input (S208). You may make it do.

  In addition to the above, the abnormality determination conditions in steps S204 and S205 may be determined not only as abnormal when continuously changing in the same direction, but also as negative or positive. For example, in the case of oil leakage, it is determined that there is an abnormality when it continuously changes three times in the negative direction.

Further, it is preferable to give an allowable value in consideration of a measurement error or the like. For example, the oil amount = 10 liters or more relative to the reference value, that is, the oil level gauge is about 1 memory or more. Oil pressure, gas pressure = 0.1 kg / cm 2 or more with respect to the reference value, 0.5 meter or more for the combined meter, etc.
(Parameter update process)

  Each time data is registered, an approximate expression is calculated and displayed by the correlation operator 12. At this time, it is also executed for the preceding and succeeding time segments, and the approximate expression is recalculated using data of the time segments having a high correlation.

(User interface)
If each explanatory variable, objective variable, and shift time are set through the user interface screen shown in FIG. 9, the efficiency of input work can be improved.

  On the input screen shown in this figure, field measurement data and the like are input, and the data is reflected in the graph by pressing the “Data registration” button. In addition, the necessary data can be easily collected by linking to external databases such as the Japan Meteorological Agency and the Fisheries and Marine Technology Center website.

  Through this input screen, field measurement data and the like are input to the OF cable abnormality detection device 1 and reflected on the graph by simply pressing the “data registration” button, thereby improving work efficiency and processing accuracy.

  As described above, according to the present embodiment, it is obtained by multiple regression analysis which time point is optimally used from the past to the present time to determine the abnormality at the current time point, and the optimum time point measurement is performed for each temperature type. Since the abnormality is determined using the value, it is possible to determine the minute oil leakage / gas leakage with high accuracy, thereby enabling early response and treatment.

It is a functional block diagram of an OF cable abnormality detection device according to an embodiment of the present invention. It is a data block diagram of the input data file 31 of FIG. It is explanatory drawing of the example of calculation of the correlation for every temperature classification and shift time by embodiment of this invention. It is a data block diagram of the shift time file 32 of FIG. It is a flowchart which shows the process sequence of the prediction formula calculation by the correlation calculating means 12 of FIG. It is a route schematic diagram by the Example of this invention. It is explanatory drawing of the explanatory variable setting by the Example of this invention. It is a flowchart which shows the procedure of the sign determination process of the oil leak / gas leak by embodiment of this invention. It is explanatory drawing of the user interface screen by embodiment of this invention.

Explanation of symbols

DESCRIPTION OF SYMBOLS 1 OF cable abnormality detection apparatus 2 Input part 3 Output part 10 Calculation part 11 Input processing means 12 Correlation calculation means 13 Predicted value calculation means 14 Abnormality determination means 15 Alarm output means 30 Storage part 31 Input data file 32 Shift time file 33 Calculation result Save file

Claims (4)

OF cable anomaly detection that calculates the prediction formula by multiple regression analysis with the oil quantity, oil pressure, or gas pressure as the target variable and one or more temperature types as explanatory variables, and monitors the abnormalities of the OF cable using this prediction formula A device,
Means for inputting measured values of the objective variable and the explanatory variable;
Correlation calculating means for calculating a target variable using measured values sequentially shifted in time for each temperature type, calculating a correlation with the measured value of the target variable, and calculating an optimal time from the calculation result;
An indication of abnormality based on the difference between the predicted value of the target variable calculated based on the prediction formula using the measurement data before the time obtained by the correlation calculation means for each temperature type and the measured value of the target variable. Determination means for determining;
An OF cable abnormality detection device comprising: A method of calculating a prediction formula by multiple regression analysis using an oil amount, oil pressure, or gas pressure as a target variable and one or more temperature types as explanatory variables, and detecting an abnormality in the OF cable using the prediction formula. ,
Inputting measured values of the objective variable and the explanatory variable;
A correlation calculation step of calculating a target variable using measurement values sequentially shifted in time for each temperature type, calculating a correlation with the measurement value of the target variable, and calculating an optimal time from the calculation result;
An indication of abnormality based on the difference between the predicted value of the target variable calculated based on the prediction formula using the measurement data obtained before the correlation calculation step for each temperature type and the measured value of the target variable. A determining step;
An OF cable abnormality detection method characterized by comprising: The target variable is an oil amount,
The temperature type includes indoor air temperature such as terminal junction box or oil supply pipe, ground temperature in land such as cable / intermediate connection box, and sea floor underground temperature,
The said indoor air temperature uses the present temperature, the said land part underground temperature uses the average temperature of the previous day, and the said sea floor underground temperature uses the sea water temperature of the previous day, It is characterized by the above-mentioned. OF cable abnormality detection method. The target variable is gas pressure or hydraulic pressure,
The temperature type includes indoor air temperature such as terminal junction box or oil supply pipe, ground temperature in land such as cable / intermediate connection box, and sea floor underground temperature,
3. The indoor air temperature uses a current temperature, the land part underground temperature uses an average temperature of the previous day, and the sea floor underground temperature uses a current sea water temperature. OF cable abnormality detection method.